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Why Dr Matt Lambert feels that positivity rate is the best predictor

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As India enters Unlock 3.0 from August 1, the third phase of exiting the national lockdown, several states, on the advice of public health officials, have preferred to be more conservative and extend the lockdown in areas with high caseloads. This approach is echoed by experts like Dr Matt Lambert, a practicing emergency medicine physician based in Washington DC, who has a ringside view of the COVID-19 pandemic in the US.

 For example, he points out that even though the Major League Baseball had a COVID-19 plan, they were side-lined within days after several players tested positive. Therefore he believes that is inevitable that we will see a similar pattern if schools reopen and predicts that they will be closed by Halloween due to COVID-19 outbreaks.

As we debate similar questions across countries, Dr Lambert explains why following the positivity rate has been the best predictor to ease restrictions and the role of technology in this pandemic. Excerpts from an interaction with Viveka Roychowdhury

 Are there any indicators which public health officials can track, to help them make decisions on how and when they can ease lockdown restrictions? For instance, should we consider wider antibody testing? 

In a world with unlimited resources, antibody testing each one of us would be a great strategy.  But given resource constraints and the limited accuracy of the current antibody tests, it has proven not to be a practical solution.

Thus far, following the positivity rate (the percentage of tests performed that return positive for the coronavirus) has been the best predictor. It corrects for the total number of tests and doesn’t lag as much as hospitalisation fatality rates.

Public health officials should continue with mask mandates and social distancing measures until the positivity rate is less than five per cent.

Can the case fatality rate be an indication? 

Case fatality rate is the most concrete measure of adverse outcomes from COVID-19, but also the most lagging. Given the incubation period of the virus, the lag in testing time, and the often brutal, protracted ICU courses for the sickest patients- fatality rates can lag up to 6-8 weeks.

Most disease progression models are forecasting increased infection rates and we are seeing re-infections in countries like China, and in India in states like Kerala, which were being celebrated as outliers in tackling the infection in the early stages. How can technology help predict or give insights into population health measures? 

The more we increase our interactions, the more cases we will see. Given that, technology has been very helpful in case modeling the number of cases we can expect.

India has been one of the fastest adopters of cell phone tracking technology that can alert you when you are near the phone of a person who has tested positive for the coronavirus. Privacy concerns have limited this technology in some countries, but it can be a useful tool to enforce social distancing.

The early assumptions were that children are protected due to their immunisations but today we know that they could be carrying home the SARS-CoV-2 virus, as asymptomatics to family members. This is particularly important in India where we still have a fair number of families, with three generations living together. How this situation can be addressed? 

Multi-generational housing has proven to be a big factor in the rate of transmission and has contributed to higher caseloads in certain social groups in the US and around the world.

Given the limited ability for those in the same home to social distance, masks become even more important. The role of children in coronavirus spread needs to be carefully considered when planning for the school year.

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