Covid in India: Why the strategies need to change

Let’s us do some simple maths. Readers who dislike maths can jump straightaway to the section “What does the math tell us?” Here you can grasps the inferences by common sense logic. Doing maths of course needs a set of assumptions. The assumption here is that the infection is a probabilistic process, a fair, logical and often used assumption. The control strategy followed so far is that whenever an infectious individual is found, he/she is quarantined. The contacts are traced as much as possible, tested and quarantined if positive. This practice can reduce the probability of spreading the infection from this infectious unit. For every infectious unit found, you follow the same procedure. There is some probability that some virus leaks out from the quarantine room, some contact remains untraced and so the infection spreads.

Let p be the probability that you successfully prevent spread from this unit. There are n number of such units so the probability that you successfully prevent spread from all the units is pn. If you achieve this, the epidemic ends there. If you don’t, the virus escaping from the n.(1-p) units creates X new units each. So the most probable number of new units created is n.(1-p).X. If this is less then n, the epidemic slowly vanishes, if greater then n, it spreads.

So far it is simple. But there is one more twist to this. You have a system that would do the contact tracing. The system has a finite capacity. So if n is very large, the efficiency of contact tracing suffers a little bit. If the epidemic is spreading, n increases exponentially whereas the system may grow at the most linearly. So the contact tracing efficiency will almost inevitable diminish as n increases. As a result p is not independent of n. But we will make p fairly robust to changes in n by using an equation p = k/(k+n) where k is an index of the efficiency of the system. If you want to slow down further the effect of n on p, a little more fancy equation p = km/(km+nm) can be written where greater the power, slower is the decrease in p with n but to suddenly drop when n is close to k. With this form the intuitive meaning of k is how many number of cases the system can handle without much loss of contact tracing efficiency. If k is large, a substantial increase in n is tolerated without proportionate reduction in the efficiency of contact tracing. But at very large n the efficiency per infectious unit suffers thereby decreasing p. I simulated with a starting n at t=0 and a set of assumed X and k to calculate p and accordingly what will be the expected new n  at t+1. The new n changes p again in the next time unit and so on.

One can increase k by imposing strict quarantine added by general lockdown and other restrictions so as to keep p high. So when and with what probability can I drive the virus to extinction by these measures?

Simulations show that the success works with a threshold phenomenon. At a given k, you can arrest the infection effectively and drive the virus to extinction up to a threshold value of n. If the starting n is higher than the threshold, arresting the infection becomes impossible. You may still stop the infection by increasing k, that is trying to make the restrictions more and more rigid. But the required increase is not linear, for a desired increase in threshold n, K has to increase in a power relation. At m=1 it increases in square proportion, the power being smaller at higher m.

What does the maths tell us?

So whether one can successfully drive a virus to extinction depends upon the parameters X, i.e. if a virus leaks from an infectious unit, how many new units it can create; k, the efficiency of quarantine and contact tracing and thirdly the starting n, the number of infected. The two most important inferences of the maths are (i) there is a threshold n above which control of the epidemic by quarantine and lockdown becomes impossible at any given context. (ii)  this threshold n  can be increased by increasing the strictness of quarantine and lockdown measures, but for which the efficiency needs to increase in square or some such power of n.

Now even if we admit that the first lockdown in India was not very efficiently executed for whatever reasons, we can’t say a more efficient lockdown will work better because now if the n has increased by 1000 fold, the efficiency will have to increase several thousand or a million fold to drive the virus to extinction.

Forget about the actual parameter values. It is necessary to appreciate at least qualitatively that the efficiency of lockdown required to drive the virus to extinction increases disproportionately more than the target n. In India, owing to high population density, X is expected to be high. If the first lockdown did not drive the virus away even at a lower n, now at several orders of magnitude higher n, it is impossible to stop the transmission. So the focus needs to shift from stopping the spread in the general population to specific care of the high risk group at both preventive and treatment level. Fortunately for us, the death rate has been coming down consistently and rapidly. Better medical facilities and better patient care can bring it down further. Disinvesting from strategies that are not going to work will allow us to invest more in the more promising strategies.

कोव्हिड: आकडे आणि धोरणं

कोव्हिडचे आकडे काय बोलताहेत हे आपण मागल्या लेखात पाहिलं. पण आकड्यांचं हे बोलणं न ऐकताच अनेक धोरणं आखली आणि राबवली गेली आहेत. ही गोष्ट भारतापुरती मर्यादित नाही हे आवर्जून सांगितलं पाहिजे. सगळ्या जगातच आकड्यांचा अडाणीपणा भरपूर प्रत्ययाला आला आहे. आपण इथे विचार मात्र जास्ती करून भारताचाच करणं नैसर्गिक आहे. कोव्हिडचं स्वरुप सुरुवातीला वाटलं होतं त्यापेक्षा खूपच कमी घातक आहे आणि दिवसेंदिवस त्याची घातकता आणखी आणखी कमी होत आहे याची आकडेवारी आता सर्वांसाठी खुली आहे. जर रोग भयंकर असला तर उपाय त्रासदायक असला तरी करावा लागतो. प्रत्यक्षात तो वाटलं त्यापेक्षा एक दशांशानेच घातक आहे. त्यामुळे आता या रोगाविषयीची आपली धोरणं बदलायला हवीत. लॉकडाउनचा उपाय हा पोटावर बसलेल्या माशीला तलवारीने मारण्यासारखा आहे. जोवर कँसरला आळा घालण्यासाठी तम्बाखू आणि सिगरेटबंदी होत नाही तोवर सरकारनी कोव्हिडला आळा घालण्याच्या उदात्त हेतूनी पुनश्च लॉकडाउन केलं यावर कुणी दूधखुळाही विश्वास ठेवणार नाही.

आकड्यांना विज्ञानात महत्त्व असलं तरी सगळ्याच गोष्टी आकड्यांमधे पकडता येत नाहीत. विज्ञानात कुठलीही गोष्ट वस्तुनिष्ठ पद्धतीनी दाखवता आणि मोजता येण्याला फार महत्त्व आहे. पण प्रत्यक्षात महत्त्वाच्या असलेल्या सगळ्याच गोष्टी मोजता येण्यासारख्या नसतात. खरोखर महत्त्वाच्या असलेल्या गोष्टी मोजता येत नसतील तर ज्या गोष्टी मोजता येतात त्यांना महत्त्वाचं मानायचं असा एक मोह वैज्ञानिकांना होतो. त्यामुळे काही काही गोष्टी महत्त्वाच्या असूनही त्याविषयी गरजेपेक्षा कमी बोललं जातं. अशी एक महत्त्वाची गोष्ट म्हणजे उपचार करणा-या डॉक्टरांचा अनुभव. किती चाचण्या झाल्या आणि किती पॉजिटिव आल्या एवढच बोलून चालणार नाही तर प्रत्यक्ष त्यावर काम करणा-यांना काय दिसत आहे त्याचीही दखल घ्यायला हवी. कोव्हिडच्या साथीमधे लक्षणे न दाखवणा-यांचं प्रमाण नव्वद टक्क्यांपेक्षा अधिक असल्याचे अनेक अभ्यास सुचवतात. हे प्रमाण आणखी वाढेल असं सुचवणारीही काही लक्षणं दिसताहेत. तेंव्हा टेस्ट पॉजिटिव येते का नाही, किती जणांच्या पॉजिटिव आल्या या आकडेवारीला यापुढे फार महत्त्व राहणार नाही. देण्याची आवश्यकताही नाही. सर्दी कोणाला होते याची आपण राष्ट्रीय पातळीवर नोंद ठेवतो का? जर ९५% लोकांसाठी कोव्हिड सर्दीसारखाच असेल तर त्या प्रत्येकाची चिंता का करायची? सर्दीपेक्षा कोव्हिड खूपच जास्ती घातक आहे पण तो फक्त काही टक्के लोकांना. त्यामुळे पॉजिटिव किती आले यापेक्षा नक्की धोकादायक लक्षणं कोणती? ती लवकर कशी ओळखायची? रुग्णालयात दाखल करणं कधी अत्यावश्यक आहे? कधी घरीच काळजी घेऊन चालेल? या गोष्टींवर अनुभवी डॉक्टरांनी अधिक संशोधन, चर्चा आणि प्रबोधन करणं आवश्यक आहे. ज्याला दाखल करण्याची आवश्यकता आहे अशा कुठल्याही कानाकोप-यातील व्यक्तीला सुद्धा काही मिनिटांमधे अॅम्बुलेंस मिळेल की नाही? कुठे दाखल व्हायचं? कसं व्हायचं हे समाजातल्या प्रत्येकाला नीट माहिती आहे की नाही? यावर सगळा फोकस असायला हवा. संसर्ग होणा-यांपैकी अगदी कमी टक्क्यांवर घातक परिणाम दिसतात. ही टक्केवारी दिवसेंदिवस घटतही आहे. पण जोवर ती आहे तोवर अशा रुग्णांची जास्तीत जास्त काळजी घेण्यावर आणि त्याना वाचवण्यावर भर दिला पाहिजे. जर त्यांच्यातला मृत्युदर खाली आणण्यात आपण नेत्रदीपक यश मिळवू शकलो तर बाकी लोकांत विषाणू हवा तितका बागडेना का!!

आपल्या डोळ्यासमोर कॉलरा, गॅस्ट्रोसारखी उदाहरणं आहेत. एकेकाळी यांनी माणसं, विशेषतः लहान मुलं पटापट मरत होती. या रोगांचा नायनाट मुळीच झालेला नाही. याच्या जंतूंचा संसर्ग होणं मुळीच थांबलेलं नाही. पण आता मृत्यूदर एकदम कमी झाला आहे कारण याची लक्षणं दिसली तर लगेच काय करावं याविषयी योग्य प्रबोधन झालं आहे. आणि ते देशाच्या कानाकोप-यातल्या आरोग्यसेवकांपर्यंत अगदी व्यवस्थित पोचलं आहे. म्हणजे सर्वर्थानी नाही पण उपयुक्त अर्थानी आपण कॉलरा, गॅस्ट्रोची लढाई जिंकली आहे.

कोव्हिड पसरण्याचा वेग पाहता आपल्याला संसर्ग थांबवता येईल अशी शक्यता आता दिसत नाही. पहिल्या लॅाकडाउनच्या काळात तो प्रयोग करून झाला. त्यानी काही काळ संसर्गाचा दर कदाचित कमी झाला असेल कदाचित. तो झाला असं दाखवणारा पुरावा नाही. झाला अशी आपण श्रद्धा ठेऊ हवं तर. पण व्हायरसचा निःपात करणं साधलं नाही हे नक्की. हा कुणाचा दोष नाही. भारतासारख्या गर्दी, गर्दी आणि गर्दीच्या देशात हे मुळात अवघडच होतं. पण तो ही प्रयत्न आपण करून पाहिला. आणि काही नाठाळ वगळता बहुतेक लोकांनी त्याला मनापासून साथही दिली. आता रोगाची साथ त्यापलीकडे गेली आहे. तेंव्हा संसर्ग थांबवण्यापेक्षा मृत्युदर आणखी कमी करण्यावर सर्व लक्ष केंद्रित केलं पाहिजे. एकीकडे हे प्रयत्न चालू आहेतच. पण दुसरीकडे आज किती पॉजिटिव निघाले त्याचे आकडे दाखवून लोकांना निष्कारण घाबरवलं जात आहे. आता लोकांनीच आकड्यांचे अर्थ नीट ओळखून त्याला महत्त्व द्यायचं आणि निष्कारण घाबरायचं बंद केलं पाहिजे. प्रत्यक्षात कुठलीही लक्षणं न दाखवता पॉजिटिव निघणा-यांचं प्रमाण वाढत आहे हे चांगलंच लक्षण आहे. वाईट नाही. कारण असे लोकच समाजाला हर्ड इम्युनिटीकडे अधिक लवकर पोचवतील. क्वचित केंव्हातरी अशा लोकांकडून एखाद्या वृद्ध व्यक्तीला संसर्ग होऊ शकतो हे अशक्य नाही. पण आज तरी अशा संसर्गाचं प्रमाण फार असल्याचं दिसत नाही. तसं असतं तर एव्हाना मृत्यूनी देशभर थैमान घातलं असतं. प्रत्यक्षात भारतात दररोज २५००० च्या वर मृत्यू होतात. त्यावर दिवसाला ५०० कोव्हिडचे. म्हणजे कोव्हिडनी सुमारे २ % नी देशातला मृत्युदर वाढवला आहे. हे घाबरून जाण्यासारखं नक्कीच नाही. अर्थात हे दोन टक्के सुद्धा कमी करण्याचं ध्येय आपण ठेवलं पाहिजे पण त्यासाठी आख्ख्या समाजाला ओलीस ठेवणं समजण्या सारखं नाही.

थोडक्यात संसर्ग वेगानी वाढणं ही चिंता करण्याची गोष्ट नाही. चिंता करण्याची गोष्ट ही की समाजातील ज्या व्यक्तींना कोव्हिड घातक ठरण्याची शक्यता आहे अशा वृद्ध, मधुमेही, हृदयरोगी व्यक्तींची काळजी कशी घ्यायची. म्हणजे आता आपली धोरणं साथ पसरण्याला आळा घालण्यापेक्षा जास्ती धोका असलेल्या व्यक्तींची काळजी घेण्याकडे वळली पाहिजेत.

बदललेल्या धोरणातलं पहिलं म्हणजे लॅाकडाउन ची आता कुठेच आवश्यकता नाही आणि त्याचा उपयोग होतो असा पुरावाही नाही. आता सर्व लोकांना आपला रोजगार परत मिळण्याचा मार्ग मोकळा झाला पाहिजे. क्वारंटाइन ही गोष्ट लॅाकडाउन पेक्षा वेगळी आहे. त्याची आवश्यकता नक्कीच आहे आणि अजून काही काळ राहील. पण आता कोव्हिड पॉजिटिव लोकांची संख्या एवढी वाढली आहे की प्रत्येकाला क्वारंटाइन फॅसिलिटी देणं शक्य नाही. होम क्वारंटाइनची पद्धत सुरु झाली आहेच. पण यामधे लक्षणांवर बारकाईनी लक्ष ठेवण्यावर, त्यासाठी पुरेसं प्रबोधन करण्यावर भर दिला गेला पाहिजे. एखादी व्यक्ती पॉजिटिव निघते तेंव्हा तिला जर लक्षणे नसतील तर काही व्यक्तींना ती कधीच दिसणार नाहीत, काहींना थोडया दिवसात दिसू लागतील, त्यापैकी काहींमधेच ती गंभीर होतील. तेंव्हा गंभीर केस लवकर कशी ओळखायची आणि तिला योग्य ते साहाय्य लगेच कसं उपलब्ध करून द्यायचं हा नजीकच्या भविष्यातला कळीचा मुद्दा असणार आहे. गंभीर केसला एकीकडे चांगले उपचार आणि दुसरीकडे काटेकोर क्वारंटाइन अशा दोन्ही गोष्टींची आवश्यकता आहे.

एखादी केस बिन लक्षणाची आणि एखादी गंभीर होण्यामागे दोन कारणं असू शकतात. एक तर व्यक्तीव्यक्तींच्या प्रतिकारक्षमतेतला फरक आणि दुसरं म्हणजे विषाणूमधलाच फरक. विषाणूंमधे सतत म्युटेशन, सतत बदल होत असतात. त्यामुळे त्यांची घातकताही कमी अधिक होत असते. एका बिन-लक्षणी व्यक्तीमधे या दोनापैकी कोणतं कारण काम करत आहे हे सांगता येत नाही. पण समाजातल्या काहींमधे हे तर काहींमधे ते कारण असणार हे तर्काला धरून आहे. आता आपण सर्व गंभीर केसेसना काटेकोरपणे क्वारंटाइन करत राहिलो आणि बिन-लक्षणी केसेस मधून विषाणू अधिक पसरत राहिला तर कमी घातक विषाणूचा अधिक प्रसार होईल असे उत्क्रांतीचे गणित सांगते. आणि गेल्या तीन महिन्यात सातत्याने कमी होणारा मृत्युदर या गणिताला पुष्टीही देतो.  त्यामुळे गंभीर केसेसना काटेकोरपणे क्वारंटाइन करत राहिलो तर विषाणूची घातकता दिवसेंदिवस कमी होत जाईल. सर्व केसेसना क्वारंटाइन करण्याचा कितीही प्रयत्न केला तरी नजीकच्या भविष्यात ते व्यवहार्य राहणार नाही. पण हा चिंतेचा विषय मुळीच नाही. किंबहुना बिनलक्षणी व्यक्तींनी खुशाल लोकांमधे मिसळणं दीर्घकालीन फायद्याचंच ठरेल अशी शक्यता आहे. खुशाल खेळायला हरकत नाही असा हा जुगार आहे कारण झाला तर फायदाच, आणि तो न खेळण्याचा पर्याय आपल्या हातात राहण्याची शक्यता एवितेवी दिसतच नाही. मग तो न खेळण्याचं नाटक तरी का करायचं?

असं व्यवहार्य तत्त्वज्ञान स्वीकारलं तर अनेक गोष्टी पूर्ववत होतील आणि तशा होण्यातच समाजाचं हित आहे. आता शिक्षण बंद ठेवण्याचं बदललेल्या परिस्थितीत काहीच प्रयोजन दिसत नाही. तरुण वयात कोव्हिडचा संसर्ग झाला तरी गंभीर लक्षणं दिसण्याचं प्रमाण मुळातच कमी आहे. आणि शिक्षण ही दारूच्या दुकानांपेक्षा अधिक महत्त्वाची गोष्ट नक्कीच आहे. त्यामुळे किमान महाविद्यालये आणि माध्यमिक शाळा पूर्ववत न करण्याचं काही तर्कशुद्ध कारण दिसत नाही.

आता लॅाकडाउन आणि कंटेनमेंट ची अंमलबजावणी करण्यात वेळ आणि पैसा वाया घालवण्यापेक्षा लोकांना स्वछ्तेच्या सवयी लावण्यावर भर द्यायला हवा. रस्त्यात थुंकणे आणि तत्सम अस्वच्छ सवयींना दंड करण्याचं प्रमाण वाढायला हवं. कोट्यावधि लोकांना थोडया तरी स्वच्छतेच्या सवयी लागल्या तर काही हजार लोकांचं बलिदान वाया गेलं नाही असं नक्की म्हणता येईल. कोव्हिडवर प्रभावी लस या वर्षात तरी येण्याची शक्यता नाही. विषाणूचा नायनाट करणे दाट लोकवस्तीच्या देशात शक्य नाही. हर्ड इम्युनिटी सव्वाशे कोटींच्या लोकसंख्येला यायला हवी असेल तर दोन पाच वर्षे तरी लागतील किंवा आपणहोऊन प्रयत्नपूर्वक संसर्गाचा वेग वाढवावा तरी लागेल. म्हणजे हे तीन्ही उपाय साधणारे नाहीत. आता आपण या विषाणूला स्वीकारणे, त्याच्या सकट पुन्हा जोमाने कामाला लागणे आणि अधून मधून गंभीर निघू शकणा-या आजा-यांची शक्य तितकी काळजी घेणे हाच सर्वात चांगला उपाय आहे. दाट शक्यता अशी आहे की काही काळातच इतर सर्दी, खोकला, तापासारखाच हा एक होऊन जाईल.

कोव्हिड: आकड्यांचे अर्थ

जॉन अॅलन पावलॅास या लेखकानी १९८८ साली Innumeracy नावाचं एक पुस्तक लिहिलं. Innumeracy हा शब्द तो illiteracy ला समांतर शब्द म्हणून वापरतो. त्याचं म्हणणं असं की सामान्य माणूस शिकून साक्षर पटकन होतो, म्हणजे त्याला अक्षरं, शब्द आणि त्यांचे अर्थ चांगले समजतात. पण आकडे वाचता आले तरी आकड्यांचे अर्थ मात्र बहुतेकांना समजत नाहीत. आज कोव्हिडच्या साथीच्या संदर्भात पावलॅासच्या म्हणण्याचा पावलोपावली प्रत्यय येतो आहे. सर्व प्रकारच्या माध्यमातून आकडे नुसते फेकले जात आहेत आणि त्याचे अर्थ न कळल्यामुळेच सामान्य माणूस गोंधळलेला आणि धास्तावलेला आहे.

वास्तविक मोजमाप आणि आकडे विज्ञानात खूप महत्त्वाचे असतात. पण आकडेवारी हे दुधारी शस्त्र आहे. समजले तर फारच उपयुक्त, नाही समजले तर गोंधळ वाढवणारेच फक्त नाहीत तर पूर्णपणे चुकीच्या मार्गाला लावणारे सुद्धा. वास्तविक आकडे समजण्यासाठी जे ज्ञान लागतं ते आपण शाळेतच शिकतो. त्यापेक्षा फार जास्ती गणित शिकण्याची गरज नसते. आकड्यांना काही सांगायचं असतं आणि ते आपण खुल्या मनानी ऐकलं तर सहज ऐकू येतं. पण हे खुलं मन दुर्मिळ आहे. आकडेवारी वापरणा-या बहुतेकांनी आकडे पाहण्याच्या आधीच स्वतःचं मत बनवलेलं असतं किंवा कुठला निष्कर्ष काढला असता स्वतःचा फायदा आहे ते आधीच ठरवलेलं असतं. आणि मग आकड्यांना स्वतःला काय सांगायचय ते न ऐकता आपल्या जे सांगायचय ते आकड्यांमार्फत कसं वदवता येईल असं ते पाहत असतात. कोव्हिडच्या साथीचे आकडे स्वतः काय म्हणताहेत ते पाहूया. साथीच्या रोगाच्या प्रसाराचं गणित चक्रवाढ व्याजाच्या गणितासारखं असतं. आज नव्यानी संसर्ग झालेली माणसं संसर्ग पसरविणा-यांच्या मुद्दलात मिळवली जातात आणि संसर्ग आणखी पसरतो. मात्र मेल्यामुळे किंवा बरे झाल्यामुळे या मुद्दलात दुसरीकडे घटही होत असते. जर नव्यानी संसर्ग होणा-यांचं प्रमाण घट होणा-यांच्या प्रमाणापेक्षा जास्ती असेल तरच साथ पसरते. पण जेंव्हा पसरते तेंव्हा दिवसेंदिवस रोग्यांची संख्या चक्रवाढीने वाढतच असते. आपण जेंव्हा काही प्रतिबंधात्मक उपाय वापरतो, तेंव्हा या चक्रवाढीच्या गणितातला व्याजाचा दर कमी होतो. म्हणजे रुग्णांचा आकडा वाढण्याचा दर कमी होतो. आकडा तरीही वाढत राहू शकतोच. मग आपण योजलेल्या प्रतिबंधात्मक उपायांचा काही उपयोग झाला की नाही हे आपण रोग पसरण्याचा दर कमी झाला की नाही यावरून ओळखायचं, रुग्णांच्या संख्येवरून नाही. मार्चच्या मध्यापासून ते मेअखेरपर्यंत आपण लॅाकडाउन पाळला आणि एक जून पासून बंधनं उठवायला सुरुवात केली. बंधनं उठवल्यावर रोग पुन्हा अधिक वेगाने पसरू लागला का? तर भारतामधल्या, महाराष्ट्रामधल्या आणि पुण्यामधल्या या आलेखांकडे पाहा. दररोज किती नवे करोना पॉझिटिव सापडले त्याची पाच पाच दिवसांची धावती सरासरी यात तारखेनुसार दिली आहे. फक्त त्यासाठी घातांक गणित किंवा लॅागॅरिदम वापरलं आहे. त्यामुळे ही सरासरीची आळी ज्या चढावानी वर चढते तो चढाव रोग पसरण्याचा दर दाखवतो.

आपल्याला असं दिसेल की लॅाकडाउन उठल्यानंतर हा चढाव वाढलेला तर नाहीच, उलट भारताच्या आणि महाराष्ट्राच्या आलेखात तो कमीच झाला आहे, तर पुण्याच्या आलेखात तो थोडा कमी होऊन परत पहिल्याइतका झाला आहे. म्हणजे लॅाकडाउन उठल्यावर रोग अधिक वेगाने पसरू लागला म्हणून आता रुग्णांचे आकडे वाढताहेत हे म्हणणं एकतर आकडे न समजण्याचं लक्षण आहे किंवा जाणीवपूर्वक दिशाभूल करण्याचा प्रयत्न आहे. अर्थातच परत लॅाकडाउन लादण्याची भाषाही तितकीच तर्कदुष्ट आहे हे सांगायला नकोच.

पण दुसरीकडे प्रत्यक्ष रुग्णांची संख्या वाढते आहे आणि त्यामुळे वैद्यकीय सेवांवर ताण येतो आहे ही समस्याही खरीच आहे. या प्रश्नाच्या संदर्भात साथीच्या संसर्गाचा दर महत्त्वाचा नसून प्रत्यक्ष रुग्णसंख्या महत्त्वाची आहे. पण पुन्हा लॅाकडाउन लादण्यानी हा प्रश्न सुटूच शकत नाही. संसर्गाचा दर मारे कमी झाला तरी ही संख्या वाढणारच, आज ना उद्या रुग्णालये कमी पडणारच. साथीच्या सुरुवातीला लॅाकडाउन आणण्याचा हेतू हा होता की एकदम मोठी साथ झेलायला आपल्या वैद्यकीय व्यवस्थेची तयारी नव्हती. ती करायला काही अवधी मिळायला हवा होता. लॅाकडाउन फार काळ चालणं परवडण्यासारखंच नाही कारण लोकांच्या पोटापाण्याचा प्रश्न आहे. साथीच्या सुरुवातीपासून साथ काय वेगाने पसरू शकेल याची गणिते मांडली जात होती आणि ती प्रसिद्धही होत होती. या गणितांची दखल घेऊन वैद्यकीय सुविधा किती वाढवाव्या लागणार आहेत ते ठरवून नियोजन करता आलं असतं. प्रत्यक्षात या गणितांच्या हो-यापेक्षा कितीतरी कमी दरानी संसर्ग वाढला आहे. आणि तरीही आता वैद्यकीय सुविधा अपु-या पडत असतील तर प्रशासनाला गणित समजत नाही याचंच ते द्योतक आहे. पण परिस्थिती इतकी वाईट नाही हे सुद्धा आपल्याला आकडेच सांगताहेत. सर्व जगात कोव्हिडमुळे होणा-या मृत्यूचं प्रमाण सातत्यानी कमी होत आहे. अनेक देशांत मार्च आणि एप्रिलच्या सुरुवातीला संसर्ग झालेल्यांपैकी पाच, दहा किंवा अधिक टक्क्यांनी लोक मरत होते. काही देशात १७ % मृत्यू सुद्धा नोंदले गेले आहेत. पण एप्रिल मध्यापासून सगळीकडेच मृत्युदर कमी कमी होत गेल्याचं दिसून आलं आहे. ते मान आता दोन टक्क्यांवर आलं आहे. भारतात हा दर चार टक्क्यांच्या वर कधीच गेला नाही पण तोही आता दोनच्या खाली आला आहे. संसर्ग झालेल्या प्रत्येक व्यक्तीची चाचणी होत नाही. जर लक्षणे दिसू लागली तर चाचणी करून घेण्याची शक्यता खूपच वाढते. त्यामुळे संसर्ग झालेल्या पण चाचणी न झालेल्या बहुतेक व्यक्ती लक्षणं न दाखवणा-या असतात. हे सर्वच देशांमधे कमी अधिक प्रमाणात खरं आहे. याचा हिशेब विचारात घेऊन काळजीपूर्वक मृत्यूदर काढणारे अभ्यासही आता प्रसिद्ध झाले आहेत. नेचर सप्ताहिकातल्या एका शोधनिबंधाने अशा अनेक अभ्यासांना एकत्र करून प्रत्यक्षात मृत्युदर ०.५ ते १ % एवढाच आहे असा निष्कर्ष काढला होता. भारतात चाचणी न झालेले कोव्हिड पॉजिटिव किती असतील हे नक्की सांगता येत नाही. पण त्यांचा समावेश केला तर भारतातला मृत्युदर ०.५ % हूनही बराच कमी निघू शकतो.

म्हणजे कोव्हिडची घातकता आधी वाटलं होतं त्याच्या एक दशांश एवढीच आहे. म्हणजे फार काळजी करण्याचं कारण नाही. मात्र काळजी घेण्यात हयगय करावी असा याचा अर्थ नाही. आधी वाटलं त्यापेक्षा कमी घातक असला तरी हा दुर्लक्ष करण्याचा विषय नाही. वैद्यकशास्त्राचं एक तत्त्व असं आहे की प्रत्येक रोगाचा मुकाबला केलाच पाहिजे. मग रोगी चार असोत वा चार हजार, तरूण असोत वा वृद्ध, गरीब असोत वा श्रीमंत. पण आपण आकड्यांचे खरे अर्थ ओळखत असलो तर त्याप्रमाणे धोरणं बदलायला हवीत. घरात बिबट्या घुसला तर करण्याचे उपाय वेगळे असतात आणि ढेकूण झाले तर करण्याचे वेगळे असतात एवढा तरी विवेक असायलाच हवा.

Covid: Anyone here understands numbers?

This question was triggered by the announcement of a lockdown again in Pune city. The lockdown decision must be based on a set of assumption, which I think are the following

1. During the earlier lockdown the rate of spread of the infection was under “control”, by whatever it means.

2. After the partial lifting of the restrictions on 1st of June, the rate of spread increased.

3. The facilities for accommodating patients in need of hospitalization and critical care are saturating. We can no more accommodate more patients.

4. Another lockdown will reduce the number of patients. Now let us see the actual numbers. Plotted below is the time trend in 5 day running average of daily reported cases on a log scale. So far the number of positives is such a tiny fraction of the total population, that the number of susceptibles is not a limiting factor in the dynamics. So the slope of the log transformed data is straightaway an indication of the rate of spread of the infection. It can be seen easily with the Indian data, Maharashtra data as well as Pune data that just before the lockdown was lifted, the slope had started declining due to some reason. In India as well as in Maharashtra the slope remained lower whereas in Pune the slope increased again to match the earlier slope. In no case the slope increased. This means that there is NO evidence at any scale that the rate of transmission increased after lifting the restrictions. So saying that the infection is spreading more rapidly after relaxing the restrictions is either an indication of not understanding numbers, or some deliberate motive to propagate lies.

In support of the theory that the infection is spreading faster after lifting the lockdown, they give the absolute number of patients which is on the rise. But the absolute numbers would have gone up even with the lockdown. Absolute numbers say nothing about the rate of spread. They are bound to go up even with the rates that were present during the lockdown.

I will be surprised if the politicians and administrators do not understand statistics. But I believe there are people from science and medicine advising them. At least they are expected to understand numbers. I know in Pune city there is a group of scientists, advising PMC. I had an impression that at least they understand science. But may be, I have to change my opinion now.

The cause of worry is said to be the increasing number of patients saturating the available beds. Clearly here the absolute number of patients matter, not the rate of spread. But will a lockdown reduce the numbers? Currently there is no evidence that lockdown reduces the rate of transmission. Earlier lockdown experience indicates that it certainly does not result into a negative trend in numbers. But even if we assume that it will reduce the slope, it still cannot bring down the absolute numbers. They will still be growing may be at a slightly reduced rate. At the best, instead of saturating today, the beds will be saturated after 3-4 days. So lockdown is not at all a solution to the saturation problem.

One of the important expected outcomes of the first lockdowns was that we did not have enough facility to handle a large number of patients in the beginning. The breathing period that the first lockdown gave was meant to boost up our infrastructure, create more wards for Covid patients. Many cities in the world did this quite efficiently but did Pune do it? Very early in the pandemic, there were dozens of mathematical models predicting the possible numbers that hospitals may have to accommodate in the coming months. The actual spread of the infection has been much slower than what most models predicted. And even then if we are running short of beds now, it simply reflects on the inability of the administration and their scientific advisers to read numbers. John Allen Paulos wrote a book in 1988 called “Innumeracy”. He uses the word with a meaning parallel to illiteracy. Educated people learn to read, write and understand letters, words and their meanings. But most people don’t seem to understand numbers. They may at the most be able to read and write them. Understanding numbers is a different ball game. This is ok for laymen. But I wonder, is there nobody in Pune who really understands numbers and so can come out and say, NO! Lockdown is not the solution! It is not going to work, because the problem a lockdown can potentially solve, simply does not exist. The rate of spread of the epidemic has not increased by lifting lockdown, so it is not going to come down by imposing it again.

More evidence that Covid-19 is becoming milder

Last month I posted my hypothesis that the virus responsible for the pandemic is evolving rapidly towards a less virulent virus. I was astonished that it became “viral”. My blog has a limited niche readership which usually counts only to a few hundreds. But last time the original blog post had a readership of over a quarter of a million, which does not include sharing on other social media.

I received a large number of responses and it was impossible to respond to all, so I thought I will wait for more data and return with updates along with my rebuttal to the responses. While most of the responses were positive and appreciative, there was some trolling as well, which I enjoyed. What I consider most important are the responses raising certain issues. There were two main lines of arguments

 (i) The death rates are not actually declining, the data or the analysis is fallacious. This might be either because (a) if the rate at which new infections appear is increasing and death happens after a time lag, the ratio of new Covid deaths to new infections would seem to have decreased but that does not mean death rate has decreased (b) the Indian data in which I showed a decreasing death rate are not reliable (c) the number of positives have increased because of more testing and so the death rate only appears to have decreased.

(ii) If the death rates are really coming down it might be owing to better medical care being available, not due to reduced virulence.

Fair enough. Respectfully I studied all the above possibilities and now I am giving more data from more reliable sources, with better analysis, and due consideration to alternative interpretations, all of which together shows that Covid has indeed become much milder throughout the globe. With this rate there is no doubt that it will be just like any other seasonal flue in a few weeks to few months. Moreover this trend is not explained by anything else but evolution of the virus towards reduced virulence.

Before we look at the data, let me add some clarification about the meaning of the ratios that we will use.  In an on-going epidemic, the true death rate is difficult to calculate. I used last time and will continue to use below the ratio of the number of confirmed deaths in a day and the number of new cases detected on that day. The term case fatality ratio is commonly used for the ratio over cumulative data. I am using daily data so I will use a new term ND/NC for the ratio of the daily new Covid deaths by the daily new positive cases. I will use one more ratio here, ND/NR that is the ratio of number of deaths in a day to the number of cases declared as recovered on that day. The two indices reflect death rate, no doubt, but they are also affected by a few other factors.

One gift of the pandemic is that almost everyone has by now heard of R or R0, the rate at which the infection spreads. There is most often some time gap between the day of diagnosis and day of death. There is a larger time gap till a person can be declared recovered. Because of the time gaps, in a growing epidemic with R > 1, the ND/NC ratio is less than the death rate and the ND/NR is greater than the death rate. With a downward curve or R < 1, it is the reverse. In either case, the true death rate lies between the two ratios. As long as R remains the same both the ratios are directly proportional to the death rate. So a time trend in either of the ratios reflects time trend in the death rate. If death rate is constant, the ratios remain constant. However, if R changes, the relationship changes transiently. Only if R keeps on changing with time, the two ratios show a time trend even when death rate is constant. If R is decreasing continuously ND/NC ratio will show an increasing trend but ND/NR will show a decreasing trend even when the true death rate is constant. If R is increasing continuously with time, ND/NC ratio will show a decreasing trend but ND/NR will show an increasing trend. If on the other hand death rate itself is changing, then both ND/NC and ND/NR will show a time trend in the same direction. So if both the ratios are decreasing it is sure sign of death rate decreasing, but if one has a decreasing and other has increasing trend, it is because of a changing R. Thus the use of two ratios instead of one gives us greater insights into what is causing an observed time trend.

Almost throughout the globe the R value for Covid 19 has been decreasing with time. In other words the days required for doubling have been increasing. So if the death rate was constant, we should have seen an increasing ND/NC ratio. It does show an increase in some countries like Canada that have achieved an accelerated downward epidemic curve. This does not necessarily mean the death rate increased in those countries. But if ND/NC is decreasing in spite of reducing R, then it is almost certain that death rate is decreasing. This is the picture in global data pooled over all the countries. Globally we see that both ND/NC and ND/NR are consistently decreasing since mid April. One more important global pattern is that the proportion of active cases needing critical care has also been decreasing.

One of the objections to my analysis last time was that data are not reliable in many countries either due to lack of transparency such as that suspected in China or data mismanagement that we have witnessed in India recently. But we can always look at data from countries which are democratic and whose data transparency has not been questioned. Illustrated here are patterns from a few representative countries showing increasing, stable or declining infected populations.

Most notable is Sweden, a country which did not forcibly impose lockdown. Although Sweden witnessed a high rate of infection along with high death rate initially, the decrease in ND/NC has been one of the steepest in Sweden. Germany has both downward curve as well as decreasing ND/NC ratio which is a sure shot indicator of decreasing death rate.

India needs a particular mention since last time I illustrated it as an example case of decreasing ND/NC. The decrease that time was very steep and if that rate continued, it appeard that by first week of July it will become just another flu, in terms of its mortality rate.  I also said that I suspect the slope will not remain the same. In reality the ND/NC for India increased significantly between 26th May and 11th June and then started declining again. There is an interesting possible reason for the abrupt rise. In India and Maharashtra in particular, from where the largest chunk of deaths come, the infection curve took a very strange turn by the end of May. This was the time when lockdown was lifted and everyone feared that the already upward infection curve will become extremely sharp. But instead it suddenly became flatter, remained flat for quite some time and then increased, but much slower than expected. Which means R underwent a dramatic drop, which is expected to increase ND/NC ratio transiently. That seems to have happened. But ND/NR kept on declining monotonically suggesting that the death rate continued to decrease.

There are problems with the Indian data. By mid June, over 1500 deaths that were declared as non-covid deaths earlier were reclassified as Covid deaths but then they were added to the data not on the day of death but on the day the classification was changed. As a result the time trend is completely messed up. If one tries to correct for this bias, the number of deaths on the left will have to go up by an average 14%. If such a correction is made, both the ratios will decrease more sharply. So although the data are messed up, there are reasons to believe that even in India the death rate has been going down.  

What is surprising is that on lifting the lockdown, the curve did not go up more sharply. It actually flattened a little. Most people tend to give the credit for flattening any curve to the quarantine, social distancing, sanitization and other preventive measures. Here you see the reverse. When people’s movement, transport, crowding etc. increased and preventive measures relaxed, R decreased further. If it was not because of better preventive measures, what could be the alternative cause? The proportion of infected individuals is still a tiny fraction of the population, so herd immunity is unlikely to have played any role. Again I suspect, the virus itself had changed by the time the lockdown was lifted.

Can the apparent decrease in ND/NC be just because of increased testing so that we are detecting more of the asymptomatic cases? I have two responses to this. One is to look at whether the testing has disproportionately increased. In India, they started with testing the contacts of known positives and the high risk groups. As the number of positives increase, this number of tests is expected to increase more or less proportionate to the number of positives. If testing had really expanded to go beyond and sample random individuals from the general population, the ratio of number tested and number positive should change substantially. Also since the proportion of positives in the general population is expected to be less than the contacts of known positives, an effort to expand sampling will inevitably result into a decrease in the proportion of positives. Using this indicator we do not see any evidence of testing having increased, because the proportion of positives has marginally increased instead of decreasing.

But even if we assume that the testing has really expanded and we are finding more asymptomatic cases and that is the reason why the ND/NC ratio is coming down, the question remains, from where did so many asymptomatic cases come up? Either they were there in the same proportion right from the beginning or they increased recently. The former means that the death rate was never high. The virus was never as dangerous as we were made to believe. Whether intentionally or not, people have been fooled about the threat of the virus all the time! If we take the latter possibility, if so many asymptomatic cases have come up suddenly, that itself is evidence that the virus is losing virulence and most of its infections do not even make you feel sick.

What if the death rate is truly decreasing, but that is only because better medical facilities are available. This sounds logical but the data look strange. Across countries, the ones with poor infrastructure and absence of good medical care have much lower death rates than developed countries that always had excellent health care. So associating good medical care with lower date rate has problems. There is no effective drug available as yet that would make a large difference. Even the promising clinical trials so far have marginal effect sizes. So how far treatment can change the death rate in the current situation is doubtful. You may say that the high death rate in developed countries is due to having greater proportion of elderly in the society. This is the factor that explains why there is high death rate here. But when the death rate in these countries reduced from over 10 % to less than 3 %, did the proportion of elderly changed this much? Or did any new treatment suddenly became available that made this difference? There is no way treatment can explain the reducing death rates. Even more convincing is the trend in the proportion of patients requiring critical care. That itself has come down substantially. So evolution of the virus to lower virulence is the only sensible explanation for it.

If the virus is really losing virulence, how far are we from calling it just another flu virus. The answer to this question might be different in different countries. Since there is little transmission across countries currently, virus evolution may have taken different speeds in different areas. Globally the proportion of patients requiring critical care has come down and at present, only 1.4 % of active cases are on critical care. Not all of them will die. This means over the next week or two the ND/NR ratio would have come down below 1 %. For seasonal flu the death rate ranges from 0.1 % downwards. Which means Covid is rapidly approaching the status of any ordinary flu.

Another indication comes from CDC data on the causes of death in the US. Influenza-pneumonia together cause 5-7% of total deaths in the US. By mid April, this had gone up to 27% mainly due to Covid. Last week it was back to 6.9 % again which is hardly different from the baseline. CDC report says that last week’s proportion may be somewhat underestimate so do not consider the basal level to be back as yet. But the trend shows that it is almost back.

In India, the average deaths per day are of the order of 50,000 and on an average about 6 % are due to respiratory infections. So the daily respiratory deaths are of the order of 3000. Covid daily deaths are of the order of 300 to 500 per day currently. So we are about 15 % above the average in terms of death. This will increase transiently since the number of new infections is still going up. But with both R and ND/NR ratio decreasing, the number of deaths are unlikely to approach what many mathematical models had predicted earlier. So although we still have time to call Covid just another flu, we are certainly going in that direction.

Now, a million dollar question. If the data are so clear, why is nobody talking about reducing threat from the virus?

I can perceive many reasons. One is a genuine worry that if we say so, people will become even more irresponsible. But this is happening all over the world anyway. There are two possible ways in which people will act in near future. One is that they realize that the virus is much less of a threat than what we believed earlier, so there is no need to worry, but it’s good to be cautious not for one’s own safety but for the elderly and weaker ones. So some responsibilities and care need to continue. The other possible way is that they stop believing in WHO and other health authorities and choose their own ways to go about. The first is obviously more preferable and that will happen if the authorities accept reality, change the policies, take people in confidence and tell them why they need not worry now but still need to be responsible in certain ways. It is also important to be prepared for a second wave if any, or may even be a new virus. So we need to learn from experience and chalk out long term policies. If the authorities do not take such a clear stand and continue to play ostrich, people are bound to choose the other way, the signs of which are already seen in all parts of the world.

There are more reasons why scientists won’t talk about the obvious time trends. Admitting that the virus is milder now is equivalent to losing huge funding opportunities. Huge amount of investment has been made in vaccine and drug development. This I would say is valuable as a back-up. In case the trend changes or there is a second wave we need to be prepared for the worst. But that is not the way it is going. It is going as a big business plan. People in this business will be most reluctant to accept that the virus is becoming milder. So partly for genuine reasons and partly for business scientists and health authorities will refrain from accepting reality.

But common man should. All data are in the public domain. Anyone can look at the original data and ask his/her own questions rather than getting fooled authorities, by media and fall prey to Zohnerism.

Open data sources used for this article:

Covid 19: We need neither vaccine nor herd immunity

I had said in an earlier blog article that the SARS Cov-2 virus responsible for the current pandemic is likely to evolve rapidly towards reduced virulence. The reason why I expect this is that on the one hand, almost all countries are implementing strict quarantine measures for all detected positive cases. But on the other hand, we cannot afford to do mass testing, leading to many undetected asymptomatic cases roaming around and spreading the virus. The virus reaches huge populations and also has a high mutation rate, so all possible variants will keep on arising. A virulent strain is most likely to cause severe infection which will invite testing and ultimately quarantine. A mild variant, on the other hand is more likely to lead to asymptomatic or mild symptomatic infections which are more likely to escape screening followed by quarantine and therefore keep on spreading. In several generations of the virus, which is a short time for us, natural selection will favour the mild variants.

While all research on the virus is engaged in developing vaccine, studying pathogenic mechanisms or suggesting treatments, nobody seems to talk about evolution of the virus. This is for two reasons. One is that people in medicine are never trained to think of evolution. The other is that virulence is difficult to quantify. It is easier to sequence the virus, study its proteins, look for antibodies in the host etc. Researchers typically do what is easy to do rather than what is scientifically more relevant. Since one cannot measure a change in virulence easily, nobody will even talk about any hypothesis related to it. This is what I call “evidence bias” in science. If it is difficult to find evidence to either falsify or support a hypothesis, people will avoid talking about the hypothesis because it cannot make a paper. Whether the hypothesis is relevant to public health is not an important issue, whether you can publish a paper is.

But in the epidemiological trend at the global as well as the Indian scene, there are definite signs of reduction in virulence. Although the infection is growing, the death rate is consistently reducing with time. Look at the patterns. From mid-April, although the total number of new cases per day has been increasing, the total reported deaths per day is decreasing.

The same is happening in India too. In fact, the case fatality rate in India was always low and it is decreasing further, although the absolute number of deaths per day has not started decreasing yet.  

Data from:

I plotted the time trend in the ratio of daily reported positive cases to daily reported deaths starting from the day the new death count exceeded 50. Although there are expected daily chance fluctuations, there is a clear decreasing trend.

Data from

Now if we make a simplistic assumption that the linear trend continues, then we can come out with a prediction that in India in about 35 days, Covid 19 will remain only as dangerous as any seasonal flu. The assumption of linearity is of course an oversimplification, the slope may not remain the same throughout. The second caveat is that case fatality rate cannot be exactly equated to mortality rate. In a growing epidemic, case fatality rate is an underestimate or mortality. But that need not affect the trend. The estimate of 35 days might be too optimistic. It may take somewhat longer. But the direction is assuring. Anecdotally I heard from some of my clinician friends that the proportion of patients needing critical care is already low.

The vaccine trial and mass production is going to take many months and may not immediately become available or affordable to the masses. For the huge population of India, acquiring herd immunity is a huge task and will not happen for a year or two. But much before either of the two becomes useful for public health, evolution would have taken care of the deadliness of the virus. We need to continue quarantine and good medical care of symptomatic cases, but not be fussy about the asymptomatic ones. Because they are going to be the saviours. Let us wait for a couple of months to see whether the prophecy turns out to be true qualitatively or quantitatively. If it does, it has a long term lesson for medicine. Virulence management strategies should become an integral part of public health planning. This is not the last time that a new virus arises. This will keep on happening. Understanding of evolutionary dynamics is certainly required to manage public health.

Need to liberate science: My reflections on the SCB award.

My lifetime goal is to liberate science from science institutions and the SCB award has assured me that yes, it can be done and is worth doing.

In the history of science, a number of major advancements were achieved by someone outside the academic circles. Charles Darwin and Gregor Mendel are the most prominent examples who completely changed our understanding of biology, but who were not academic scholars in any formal capacity. A question worth asking is whether this can happen today. Science is increasingly getting monopolized by Universities and Institutes, so much so that it is almost impossible now to imagine someone doing high quality science outside academia. The monopolization of science is driven by two key processes, namely funding and publication. Most funding agencies simply do not have any norm to fund an individual researcher who has a proven record of high quality output but who is not affiliated to any institution currently. Scientific journals, on the other hand do not have any written rule of not publishing anything from a person without affiliation, but most journals now have huge author charges, so that no one without substantial funding can publish in them.

Science itself is at loss by such monopolization. Although Institutions are built to support science, they simultaneously put limits on doing science. Every institute has a set of bureaucratic rules and procedural routines and they expect science to fit into these routines. Science needs to modify itself to fit into the routines rather than the routines being flexible enough to accommodate the requirements of research. The routines are designed to support certain types of research requirements and researchers doing those types can happily work within the system. But by definition research cannot be bound by routines. There are no routine questions in research, only questionable routines. Institutes can cater for research routines easily and it is extremely difficult to do creative science within those stone walls.

For example, in order to purchase equipment there is a procedure in any institute, which is designed for some of the equipment to be used within the institute. I had a behavioural intervention based project for farmers in a farmer-wildlife conflict area. I wanted the farmers to install solar fencing, for which funding was available in a research project with me. But I wanted the farmers to decide which equipment they wanted and how would they like to purchase it. This was behaviourally important. If they take a decision themselves, they are responsible for the decision. If the researcher takes a decision for them, they are not. The behavioural consequences and the outcomes would obviously be different. However, since the funding was routed through the institute, the institute said you should follow the institute procedure for purchase. We will invite quotations and we will purchase for the farmers. They cannot have any say in the purchase procedure. There was another caveat, the equipment will belong to the institute and when the project was over, farmers will have to return it. This was just not compatible with the behavioural intervention principles on which the project was based. So either I had to compromise with my science or let go the funding and spend the necessary amount from my pocket. Needless to say, I preferred the latter.

The example is not the only one by which institutes arrest the spirit of science. They have multiple ways of doing it. Since there is a limit to which institutes can pursue good science, we need to look beyond institutes to implement any novel, out of the box, creative and socially useful science. For a large part of my career I worked as a teacher in an undergraduate college, where one is supposed to teach science, without actually “doing” science. I thought this was impossible, and the only way to effectively teach science was to DO science in partnership with the young minds. Later, for 10 years I worked in a leading science institute which was also supposed to teach science. Here I experienced that although there were many good researchers here, the ones having a vision for using it to inculcate the spirit of science were exceptionally few. I think now, that this is an inevitable result of the way we have been “institutionalizing” science. I was convinced even more when I decided to liberate myself from mainstream science organizations and started working in an individual capacity in association with people and without being affiliated to any organization. It was a blessing in disguise that I left “formal” science.

When you start working with people, entirely new avenues of science open up for you. Illiterate people are great mathematicians. They have their own ways of modelling, calculating, judging and making decisions. They have an inbuilt intuitive economics which is different from the economics being taught at universities and often it is more insightful. They make statistical inferences which are based on a set of principles somewhat different from what is taught in University Statistics. For example trained statisticians use a fixed significance level, people appear to vary the significance cut off based on the cost-benefits of the inference, which is much more sensible. But their great strengths become their weaknesses in some contexts. Academia has so far failed to understand the evolved innate mathematical, economic and statistical algorithms of people. Academics have developed methods of statistics, economics and computation without any regard to the evolved and innate statistical, economics and computational methods of people. Now we expect them to understand our methods while we have failed to understand theirs. As a result many of our schemes intended for their development are failures. Understanding principles of people’s behaviour is a highly challenging field of science and is yet in its infancy. So doing science with people is certainly no inferior than doing high tech ivory tower science. The kind of questions you can handle in fields and forests and grasslands cannot even be imagined sitting in those ivory towers.

Understanding human mind and human behaviour is the toughest business and needs very high quality science. Unfortunately classical social sciences are bound by ideologies and isms. The science of people needs to evolve from people, not from ideologies and the only way to do this is liberation again; liberation from ideologies, from traditional mind sets, from what you learnt in textbooks, from organizational routines and from premeditated goals and protocols. I don’t have any tall claims of having discovered something great in my attempts to study people, but can certainly share my experience that it feels great to work with them when you are free from all these handcuffs.

The piece of work, which led to the SCB award is an attempt to establish a novel principle on which the foundation of community management can be laid. This idea evolved from the interaction with people. On the one hand it’s a new game in game theory; much more complex than all the classical games dealt by the theory. Dr. Neelesh Dahanukar, a former student of mine has been a valuable contributor to developing a formal model. But more remarkable is the actual experimental implementation of the game for addressing a real life problem, with real money. I am a pretty bad organizer, so I couldn’t have done the organizational part of the empirical work myself efficiently. But I was fortunate to be backed by a whole team of efficient people including Poorva Joshi of Bioconcepts, Vijay Dethe, Smita Dathe, Shankar Bharade and other volunteers of the NGO: Paryavaran Mitra, and of course all the farmers, who I consider not the subjects of my research but my research collaborators. We could do the experiment with real money owing to the generosity of Vidarbh Development Board, DeFries Bajpei Foundation and NAAM Foundation.

With the only exception of Neelesh, none of my co-workers had any formal background in research, but together we came up with a scientifically novel and socially useful outcome, certainly the first real life, real problem, real money game theory experiment in India and perhaps in the world. I have at least one case to demonstrate now where science, liberated from institutions worked well.

While this is certainly not the first example of people’s science, the tradition needs to be revived and strengthened. The trend of institutional monopolization of science needs to balanced by alternative avenues of science. While we certainly need science institutes on the one hand, an alternative to institutional science is needed to do the kind of science that Institutes and Universities can’t.

About the award:

An account of the work with farmers: In English:

In Marathi:

Old age, immunity and Covid-19

It is a common belief that immunity declines in old age. This is particularly relevant today because in the covid 19 pandemic, fatality in the elderly is considerably higher.  People believe that this is because of decreased immunity in the old age. While some markers of immunity do go down with age, the question is whether old people are really more susceptible to infections? One needs to differentiate between intermediate markers and end points. Showing that a given treatment reduces cholesterol is different from showing that it actually reduces heart attacks. Similarly showing some antibody titres going down is different from actually showing increased frequency of infections.

A few years ago, we took up this question as a “katta” question. I had 4 summer students then and they were given this exercise of how to test whether markers like declining antibody titres could be equated to increased susceptibility to infection. Does the susceptibility to infection really go up? If yes, whether there are alternative interpretations to it, and how to differentiate between the alternative interpretations? An attractive looking alternative interpretation came up, which was that the microorganisms associated with the body over a long time, evolve to overcome the resistance mechanisms of the individual within the lifetime of an individual host. This concept is not new. Bill Hamilton used it in 1980 to develop his own hypothesis for the origin of sex. Vidhya, one of the summer students took the challenge seriously and not only came up with some testable predictions of the immunity decline versus pathogen evolution hypothesis but dug up some data and tested them. Summer trainees are there for a very short period and starting with development of a new idea they can rarely complete any piece of work. I think for them, the ability to generate a new idea is more rewarding than being able to complete a piece of work.

I like to talk about ideas at all stages of development to as many people as I can. In many institutes there is a culture of keeping your ideas secret until you complete your work and publish them. I think this might be a good strategy in places where there is an overall dearth of ideas. Working with young minds, I never experienced this state, so I talked about what Vidhya did, to many people around. Then Akanksha, who had done a lab rotation with me but had joined another lab that time, thought that it was worth pursuing the idea further. So she and Ulfat, my long time co-worker, did more systematic data search, involved Vidhya once again and started digging more data, analyze and test the alternative hypotheses across a wide diversity of pathogens. All this was not a priority work, so it took almost 5 years to complete and publish the work, which happened just over a few weeks ago.

What the data essentially show is this (see figure). With age the incidence of all opportunistic pathogens increases substantially, often 10 to 30 fold. But the incidence of externally acquired infections, that is infections by pathogens which do not remain associated with the body over a long time, does not go up; on the contrary reduces with age.  If there was a decline in general immunity, the incidence of all types of pathogens should have gone up, may not be to the same extent, but the direction should have been the same. But some infections appear to become more common and some substantially less common, and there is a clear demarcation. Those by opportunistic pathogens go up. Those by externally acquired pathogens go down or don’t change. This pattern is compatible with the pathogen evolution hypothesis and not very supportive of general decline in immunity.

Age specific incidence of opportunistic pathogens (group 1) and externally acquired infections (group 2). Group 1 infections have a clear tendency to increase with age where as group 2 decline with age or don’t change (Baig et al J. Bioscience 2020).

There was another interesting pattern observed in the data. Although old people do not get outside infections more commonly, once infected, they are likely to collapse faster. This we think, is because of a general physiological decline, rather than an immunity decline. If it were immunity decline, it should have affected incidence as well as morbidity. But it does not increase incidence. Selectively increases morbidity and mortality among the infected ones. This indicates that the physiological ability to cope with infections declines with age, not the immunity. If this is true, what should matter more than age is the overall physiological fitness, not the number called age. In those data, we couldn’t test this because no index of general physiological fitness was available.

When we did this work, the current coronavirus pandemic was not even on the horizon. So obviously our paper does not have any data on it. Right from the beginning of the epidemic, people have been talking about old people being more susceptible. Now we have data on age specific incidence in India till date and it shows the same pattern. The incidence actually comes down in the older age classes. One possible reason might be reduced exposure but there is another interesting possibility which I am surprised, no one seems to be talking about. Old people, who have experienced a large diversity of viruses in their life time are more likely to have cross-immunity to a new virus. So old people are less likely to be affected by a new virus.  But on the other hand, the case fatality does increase substantially with age, but that too in individuals who already show markers of physiological decline such as hypertension, diabetes, loss of cardiorespiratory fitness etc. This matches perfectly with our hypothesis.

Incidence of Covid 19 positives in India per million of a given age class. Clearly the age specific incidence declines at higher age classes. Old people have a SMALLER chance of contracting a viral infection, but they can collapse faster if infected. Data source;

Looking at patterns in data and making a hypothesis to explain that is one thing. And developing a hypothesis based on theoretical considerations first, raising alternative hypotheses simultaneously and then systematically looking at data to compare the alternative hypotheses is another. The level of scientific rigor in the latter is substantially higher. In published data, it is difficult to know the path. People may actually have followed the former but pretend the latter. But in this case we already published our hypothesis, along with supportive data.  And now a new global picture emerged which complies with our hypothesis very well. This is heartening. The take home lesson is that for old age care, fitness matters more than actual age for so many communicable as well as non-communicable diseases. The exception is opportunistic pathogens. We cannot stop them evolving. So that effect of age is inevitable and so are those infections. But otherwise fitness is the key to overcome aging and age related diseases. In the long run, caring for the fitness of the elderly is a better approach than trying to protect them from external sources of infection.

Impact factors, citations and one’s own perception:

I published close to a hundred research papers, mostly in peer reviewed mainstream journals but at times also in some so called predatory journals. For me, particularly at this stage, all are interesting experiences in different ways. A simple thing I am going to do now is to look at the ‘objective’ measures namely citations per year and the impact factors of the journals in which they were published on the one hand; and my own perception and subjective judgment of the importance of that work on the other. Do the three have anything to do with each other?

Here are the scatter diagrams of the three. I must state first that there were two outliers which are not included in these diagrams. My most cited paper, one with close to 1000 citations was published in 2001, making citations per year close to 50. It was published in a journals that has a an impact factor of 1.8 today and it has remained more or less the same over many years. The other extreme is another paper published in 1997 in a journal that has an impact factor of 59 today. But in 23 years, it was cited only 17 times. So the two extremes are actually upside down of the expected relationship,  but let us leave them out being outliers. The rest of the papers make scatters that look like this. Needless to say they do not show any correlation with each other.

Relationship (or the lack of it) between the 2019 impact factor of the journal in which a paper was published and the number of citations received per year so far: The impact factors are based on the average number citations received by the papers in a journal and therefore at least a weak positive correlation is expected with the number of citations in the sample. But my sample does not show any. Nevertheless the average agrees because if a line with a slope of one is drawn, half of my papers lie above it and half below, more or less symmetrically.
My own rating of our papers is not correlated to the impact factors of journals where it ultimately got published. The experience summed up over a few hundred acceptances and rejections is that it is a random event and has nothing much to do with the quality of the paper.
My own rating is also not correlated with the number citations received.

A typical researcher thinks first of the highest impact journal in which a given manuscript is likely to get published,  if it gets rejected there, he or she goes for a little lower one and so on until it finally gets published somewhere. I did the same at times, but not every time. Life is an opportunity to experiment and I experimented with my own papers in a variety of ways. This included publishing in some so called predatory journals too, and my experience there, particularly about the sensibility of reviewers’ comments, was not very different from high impact mainstream journals. No doubt, the degree of sophistication was different.

My own subjective ranking of my own papers is based on the novelty of the central idea, it’s relevance to fundamental science, its non-obviousness, logical soundness and clarity of the evidence. I made a conscious attempt not to factor in the importance given by others, the extent to which it helped my own career or that of my students. The lack of correlation with the number of citation perhaps indicates that what I thought was good science, others may not have found worth citing. On the other hand, when I looked at the papers that had cited ours, exceptionally few of them had cited them for the central argument in the paper. Often they were cited for supporting a statement that was never made in our paper. On a few occasions our paper was cited in support of exactly opposite view than the central argument of our paper.

About the number of citations received, in general, papers that more or less supported the beliefs prevalent in a field were highly cited. The ones that opposed the prevalent beliefs were not cited. This is important. There was no counter-argument ever. There was no debate, no criticism. People only seemed to ignore what they did not like. The quality of argument and evidence did not seem to matter much. This has been quantitatively the commonest response. But science does not progress by the response of the masses, I mean the researcher masses. It progresses by the outliers. Rarely someone sees a subtle point in what you wrote and appreciates, rethinks, introspects, at times attacks back, but everything in a very lively way. This is very rare, but science progresses by these rare interactions. I did experience such instances too and these are the true rewards in a scientific life. A rare, critical, agreeing or disagreeing but curious and interested response is what science progresses by, and this is not countable!! This is the reason why numerical indices might be useful in the business of a science career, but have nothing to do with science itself.

Evolution of virulence and the ethical dilemma raised by Covid 19

There has been a long held belief that pathogens, in the long run evolve to become mild, lose or reduce their virulence. This is believed to be because rapidly killing their host is against their own interest. While there have been remarkable and well documented examples where a virus, new to a host population spreads like wildfire and kills a large proportion of the population, but soon loses much of its virulence. This is certainly the story of the Myxoma virus that was introduced to control the population of rabbits in Australia in 1950.

The myxoma virus was said to lose its virulence due to natural selection because viruses that allowed their host to live longer had a better chance to spread in the population than the ones that killed the host very fast. This is not as easy as said, because it is a multilevel selection problem. Within a host a virulent variant will grow more vigorously than a mild one, but across host individuals the ones infected by the less virulent ones will live longer and keep on spreading the virus. Multilevel selection, unfortunately, has been a contentious issue in evolutionary biology, fought more with beliefs and rhetorics than with sound, realistic and context based models. But the most sensible argument has been that under one set of conditions, the within host selection becomes stronger than the between host selection and higher virulence evolves. Under a different set of conditions the reverse may happen and virulence is progressively lost. In reality we have examples of myxomatosis in which virulence was demonstrably lost, but we also have examples like small-pox virus which ultimately became extinct but never lost its virulence.

We have one more interesting case in which virulence was apparently constant for over thousand years. Then something happened and within 3-4 decades virulence was lost almost completely. This happened with leprosy. Descriptions of leprosy have been there in old literature from which it seems that it has been with us almost unchanged for several centuries. Then between 1960s and 1990s, the pictured changed dramatically. The proportion of lepromatous cases, which is the most severe form of the disease, started coming down monotonically all over the world. Milder forms of leprosy were still there for a few decades, but then they also started disappearing rapidly. This was brought about by a combination of social stigma associated with the disease and discovery of effective drugs against leprosy. One of them in the absence of the other would not have succeeded. Owing to the attached social stigma leprosy patients were not being treated in general hospitals. There would be separate asylums or at least separate wards. As a result going to these wards was to advertise that I am a leper, which nobody liked. But patients with severe form of the disease had to do that. Those with milder forms could hide their disease for a longer time and move around normally in the society. As a result the more virulent forms were selectively killed by the drug, and the less virulent ones allowed to live longer and thereby spread more. This differential selection was so strong that leprosy ceased to a problem in four decades. Effective drugs were there for staphylococci as well. But they, instead of losing virulence, developed drug resistant varieties. This could be mainly because a systemic infection as well as a small boil was being treated with the same antibiotic. In a more severe form of the disease, the germs would have replicated much more before facing the antibiotic and thereby have a better chance to spread than a milder form of infection. Here selection would favour the more virulent ones and eventually antibiotic resistant ones. This means that our practices of preventing and treating the disease as well as the social beliefs with which we view the disease influence the evolutionary course of the pathogen.

This certainly would apply to the rapidly spreading and, needless to say, continuously evolving coronavirus Covid 19. The entire world has taken unprecedented steps to prevent, or at least slow down its spread. What is rarely addressed is how would the social distancing and lockdown affect  further evolution of the virus?

If we do large scale testing, try to detect and isolate every positive individual, there is a small chance that we might drive the virus to extinction by completely preventing transmission to other individuals. But this is unlikely and does not seem to be working that way, although it may have slowed down the spread. But what could natural selection be doing under this scenario? If every positive case is in quarantine, more virulent and less virulent strains are both being isolated with the same probability. Let’s assume that every propagule generated from the quarantined individuals has a very very small chance of escaping isolation and infecting someone. Just because the virulent ones make more propagules, they have a greater chance of escaping than the milder varieties. This means we are giving a selective advantage to the more virulent ones over the milder ones and as a result virus will evolve for increased virulence.

On the other hand if testing kits are so limited that you reserve them for individuals showing considerable symptoms, the milder cases will escape detection and will live and spread. Some of the more virulent ones are also likely to escape if they happen to be in partially immune individual who can keep the symptoms suppressed. But on an average the virulent ones are more likely to be isolated than the mild ones. As the milder ones spread more, they make the population immune, preventing the spread of the virulent ones further. This, by evolutionary logic, is expected to lead to gradual loss of virulence along with increasing herd immunity.  

The logic goes very similar for the lockdown. If the lockdown is complete, and every case is effectively isolated, only the virus varieties making more propagules will survive. If on the other hand people with marginal symptoms or mild symptoms go around and mix with the population, the milder variants have a better chance to spread and immunize the population at the same time.

But all these arguments are based on probabilistic logic. Not a deterministic one, and there lies the problem. Although on an average a relaxed isolation practice will spread the milder variants more than the virulent ones, transiently more individuals would certainly be infected with the virulent ones and suffer more.

Here lies the most basic ethical problem of medicine. If there is a conflict between what is good for an individual versus what is good for the population, or what is good in the short run versus what is good in the long run, what do I do? If we just let go the virus wild, ultimately the virulence will come down as well as herd immunity will build up. But during the process many individuals will suffer. Who will take responsibility for their suffering?  

There is no simple answer to this question particularly when we have little data on the actual transmission dynamics to make quantitative evolutionary predictions. Making a good evolutionary model needs greater details on the dynamics of mutant segregation during the transmission process since multilevel selection crucially depends upon that. What I am most surprised to see is that among the multitude of arguments supporting and opposing the lockdown, there is hardly any evolutionary argument. Evolution in viruses is surprisingly fast, particularly when they are spreading on a global scale. What will be the future course of the pandemic will be decided to a large extent by how the virus evolves. But there are hardly any attempts to study the infection dynamics from the functional evolution point of view. There are some molecular evolution studies though. I don’t have final answers right away, but I am sure incorporating evolutionary thinking in the designing of strategies to manage the pandemic would make substantial difference. But the facts remains that it is not only a problem of prediction and management it is an ethical dilemma at the same time.