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It has been almost a month since my last blog on COVID-19, but it feels like a lifetime. So much ink has been poured into the ever-growing literature, both serious and peer-reviewed, and elsewhere in the news and social media. We do know much more now about SARS-CoV-2 (the virus), COVID-19 (the disease itself), and the making of the pandemic.
We have also begun to learn more about the health – both physical and mental – and socioeconomic consequences. We have come to realize that except for a few countries like Republic of Korea and Singapore the global community was caught unprepared, and as such, had to resort to drastic social distancing and lockdown of non-essential economic activity to contain the contagion.
Many countries, rich and poor alike, are grappling with severe challenges: shortages in personal protective equipment, insufficient test stocks to detect all suspected cases, and limited capacity for treating the severely sick in intensive care units.
What will be next?
All countries will now have to ride this epidemic wave with whatever public health and medical care measures they have at their disposal. A few may also benefit from the largesse of others. There is not much time to question the courses of action taken by countries around the globe, as they all are busy extinguishing the fire and learning by trial and error. Yet, there is a lingering dilemma, a yet-to-be-explicit debate on the tradeoffs.
Are the drastic measures are going too far, to the point of risking a total economic meltdown? Could there be a middle ground, a tradeoff between optimal health security and economic shutdown? Where would we draw the line? Many economists in academic circles and at the World Bank have been modeling different scenarios, which are as credible as the assumptions their models are built on.
A wealth of data
I believe epidemiology has a lot to contribute to the debate, perhaps more so now than it could have a few weeks back. First, we have now reached almost two million cases globally. Equally important are about 500,000 recovered cases, in addition to perhaps another million or so asymptomatic cases that have not been diagnosed.
Second, not a day passes without a new test being readied for rapid on-site diagnosis. This means we can increasingly diagnose cases with the now well-known Polymerase Chain Reaction (PCR) tests, which detect the virus during the period of infection (usually lasting around three weeks), but not following recovery.
But even more importantly, we are seeing the arrival of tests that detect the presence of antibodies that the immune system produces to fight infection. There are two types of antibodies: the Immunoglobulin M (IgM) antibody becomes detectable sometime during the onset of symptoms and disappears as the patient recovers, and the other, Immunoglobulin G (IgG), appears sometime during recovery but remains detectable beyond recovery – perhaps permanently and rendering the recovered patient immune to re-infection.
Third, we are operating in a novel context of “natural experiment,” where mobility is mostly –and in some places, totally – restrained by social distancing and quarantine measures, preventing international and even domestic travel.
We have an unprecedented opportunity to use both tests to precisely estimate the distributional patterns and trends of COVID-19 cases through mass screening or random surveys and sentinel site surveillance. These tools can provide us with a thorough analysis of cases broken down by key epidemiologic characteristics of the infected, asymptomatic and recovered by location.
This data could help us determine whether we really must resort to a total lockdown, for example, in a country of islands where most of the cases are located on one. We needn’t have the same drastic measures in all provinces if patterns in case distribution could allow authorities to quickly circumscribe the “hot spots,” alleviating the socioeconomic consequences of the pandemic.
Tailoring quarantine measures to specific locations
It is time to gradually move away from aggregate numbers and rates. We need to take full advantage of the large and growing case numbers to pinpoint specific geographies for containment efforts – putting the finger where it hurts and relieving pressure from where it does not – making full use of the notion of a cordon sanitaire.
I would defer to modelists on whether location-specific containment efforts might have consequences for the evolution of herd immunity and the flattening of the curve. Many location-specific curves might be more relevant to forecasting unmet needs in the health workforce, ICU beds, and ventilators, and enabling better-informed decisions on social distancing.
Way back in 1854, John Snow, an inquisitive physician living before the era of germ theory, was able to identify a source of the cholera outbreak in London by mapping out all the cases with a piece of paper and a pencil. This allowed authorities to remove the handle of the culprit water pump on Broad Street instead of those of all water pumps in the city. In an age of spatio-temporal modeling and small area estimation, we should be able to do the same as Snow, mapping COVID-19 across diverse geographies by drawing on big data and massive computational power.
I think it is worth a try, given the alternative of total nationwide lockdowns and the threat of new epidemic waves on the horizon.
What do you think?
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@PAHO Health Emergencies we are aiming at providing this kind of advise to the countries in the region of Latin America and the Caribbean, accompanying countries and ministries of health in their decision making and criteria needed for lifting certain measures, even at subnational level.
Can you be more focusive by pointing out the supporting or feed-backing you are looking for?
Very engaging post, Enis! Snow showed us the road with his old ink map of London. Today, in addition to spatio-temporal modeling and small area estimation, artificial intelligence can manage real-world data of patients. Thus, it will be relatively easy to compile information at individual level regarding: (1) basal conditions; (2) previous and actual medicines; (3) demographic characteristics; (4)... vaccination status, etc. And amongst that big amount of information, we will be able to identify predisposing factors (and also protective factors). And, perhaps more important: the study of people not affected by COVID, in order to understand why they were not sick. Regards, Albert Figueras Barcelona
Read more Read lessThank you Enis for your comprehensive Blog.
Dear Enis, Nice to see that you are well and still very much engaged!! I remember with nostalgia our contacts in 1990s and again in 2000s in WHO/EURO Copenhagen! Agree fully with all you say and the importance of using data (but then I would wont I!!) to help make the very important decisions you mention (and more!). I would just like to add that we dont have to wait for data on "known"... infections which in many countries (eg the Uk) will be a bit inadequate anyway as there is no community testing of any kind (not enough kits etc and resources being prioritized to just keep head above water to save lives!). As you know, hospital admissions and PCR testing can at best show tips of icebergs! In UK even this limited testing is not done comprehensively for NHS staff nor residents and staff of care homes!). About 6 weeks ago I got very frustrated and tried myself (relearning excel!) to examine published UK ONS data to see if mortality data could be used to shed light on the sort of PH policy and Health service management decisions u mention. Unfortunately, I was too Excel rusty and changing geographic codes of published data frustrated all my efforts and so I gave up! However, the potential is huge! Because of the initiatives started by the "WHO CC for mortality" in 1990s, now many countries have tested computerized mortality systems (inc USA and UK and other European countries and I think Brazil!) that record weekly/daily (with only a few days time lags!) deaths from Covid 19 (WHO has given it a specific ICD code). In any case influenza deaths are recorded as well as geographic codes!! Therefore WHO/WB can set up a network of such countries to collate and pool such data and show the spatial distribution which would help in all sorts of decisions some that should have been taken already (but were not because I presume no body saw the potential of mortality data). You mention some of the imp decisions and there are others eg even which regions/districts to begin community testing since we will never have enough resources to do everywhere at same time. Even delivery of PPE to hospital and care homes could be prioritized now based on covid deaths. For safety I would of course also look at excess influenza (and related ICD codes) deaths and by comparing same months for previous years (after smoothing and removing outliers) try to get estimates for "excess influenza deaths" that might actually be covid 19 deaths. Such month on month analysis of excess deaths from other diseases (eg Cancer, liver etc) could also shed light on ensuring that we dont fall into trap of saving covid 19 deaths at the expense of increased deaths resulting from diversion of beds/staff and interruption of existing treatments for these diseases, not to mention not seeing/admitting new patients into the system. Like you I leave issues re herd immunity to others although as you say we still know very little about this novel virus and can only hope and pray that like other viruses heard immunity will apply. The jury is still out as what data is reported in papers from China, S Korea and others is not encouraging. BTW I got the same sort of flu 3 times from Nov to Feb and during the January episodes I told friends not to worry as it was a home grown variety! My flippancy was clearly misplaced! Keep well and safe and if u r in UK when all this is over do be in touch and we can have a beer together!! Best regards arun
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