A Second Wave imminent in Mumbai ? Implications for Covid 19 from L Ward

  • The slum areas will most likely leave COVID-19 behind them in the near term. Reinfection risk remains but our analysis provides a lead indicator of the likely path. Because the Slums were impacted most severely back in April they are now further along and in some ways a bellwether what could happen next in the non-slums
  • The non-slum areas might be at risk of a surge of infections 7x the level experienced in April. Reinfection risk remains but the analysis in the slum can help narrow the range.
  1. The most important check in all the graphs is the simulation passes through the uprated seroprevalence data point all the way at the right. This tells us that the speed of the spread in the model is in line with what actually has happened in slum areas of L Ward in Mumbai.
  2. Next most important is the model is closely mimicking the daily and accumulated deaths. Why is this next? Because deaths are clear and tangible and backed up by a medical examiner investigation and certificate. It is possible some deaths may not be recorded but that is not something we can judge. We can only track the data we have.
  3. Last in importance is the new cases. Why? Between 70–85% of COVID cases are asymptomatic: we can’t see them. But we can work backwards from Seroprevalence and deaths and the epidemiological evidence from across the world embedded in the Localisation model to make a very accurate estimate of how many people must have been infected. We have estimated using the model that the testing programmes finding new cases have discovered between 2%-4% of the actual infections. The new case data is a very inaccurate measure of what is really happening.
  4. The resulting forward view is very sensitive to reinfection risk and this is currently a major uncertainty. Assuming low reinfection risk there is a low level of new infections in the slums, then a modest increase next year because our simulation has low reinfection risk embedded. Tracking the rate of reinfections is a critical early waning metric to quantify the most likely path forward.
  1. Unless reinfection risk is determined to be significant (we examine this later), in the slums the danger is mostly past although not eliminated. Many people have been shielded; caution is still important. The remainder of the population at risk has dropped to much lower levels and it perhaps is time to try to return to a normal life.
  2. The residents of the non-slums have a different challenge but with high levels of resistance in the slums the risk of encountering an infection is much lower now than at the beginning of the outbreak. It is not straightforward what to do next and it is highly dependent on the answer to another question.
  1. We have revealed distinctive strategic insights and identified specific actions that could help better manage the pandemic for locations with very different living conditions
  2. In a short time we have learned an entirely new approach linking rigorous Data Analytics and Modelling to Policy Recommendations.
  1. Developing distinct strategies for slums and non-slums as they have different characteristics;
  2. Promote synergy between the elected representatives of the urban local bodies and ward level medical officials;
  3. Conduct health protocol awareness drives with help of community leaders;
  4. Incentivise adherence to hygiene measures by positive nudge or penalties;
  5. Enhance mechanism for door-to-door surveillance, contact tracing and isolation
  6. Rigorously record reinfections in the slums vs non slums
  7. Use the model to make better estimates of pre-existing resistance and loss of resistance
  1. Praja Foundation Elected Representatives Fellowship 2019–2020
  2. Guidance. Praja Foundation: Priyanka Sharma, Pooja Verma, Milind Mhaske, and Yogesh Mishra; The COVID-19 Localisation Modelling Group: Maurice Glucksman, Andre Nemec, Dr Kim Warren
  3. “Mumbai Covid 19 Localisation Update: Emigration has reduced new cases and prompted celebrations — It may be too soon to celebrate” Medium article
  4. The Covid 19 Localisation Modelling Group. https://covid-19-localisation-modelling.thinkific.com/
  5. Includes: https://stopcoronavirus.mcgm.gov.in/key-updates-trends; Hospital, L Ward interviews
  6. “Technical details: SARS-CoV2 Serological Survey in Mumbai by NITI-BMC-TIFR”, https://doi.org/10.1101/2020.08.27.20182741
  7. “The infection fatality rate of COVID-19 inferred from seroprevalence data”, https://doi.org/10.1101/2020.05.13.20101253
  8. We uprated seroprevalence data by 10% (from 57% to 63% in the slums and from 16% to 18% in the non-slums) as an approximation to account for people who develop resistance after an infection but do not have measurable Covid-19 anti-bodies
  9. When formulating strategy we re-scale the results to the levels expected in L Ward
  10. “Clusters of COVID-19 associated with Purim celebration in the Jewish community in Marseille, France, March 2020”, https://doi.org/10.1016/j.ijid.2020.08.049
  11. “Impact of Routine Infant BCG Vaccination on COVID-19”, https://doi.org/10.1016/j.jinf.2020.08.013
  12. “Declining prevalence of antibody positivity to SARS-CoV-2: a community study of 365,000 adults”, Imperial College Oct 2020. https://doi.org/10.1101/2020.10.26.20219725

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The Covid 19 Localisation Modelling Group aims to support education and provide analytic tools for local actions managing Covid 19

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The Covid 19 Localisation Modelling Group

The Covid 19 Localisation Modelling Group

The Covid 19 Localisation Modelling Group aims to support education and provide analytic tools for local actions managing Covid 19

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