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Register a new userPotential 3rd COVID Wave in Mumbai: Scenario Analysis
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The second wave of Covid-19 that started in mid-February 2021 in Mumbai is now subsiding. Increasingly the focus amongst the policy makers and general public is on the potential third wave. Due to uncertainties regarding emergence of new variants and reinfections, instead of projecting our best guess scenario, in this report we conduct an extensive scenario analysis for Mumbai and track peak fatalities in the coming months in each of these scenarios. Our key conclusions are - As per our model, about 80% of Mumbai population has been exposed to Covid-19 by June 1, 2021. Under the assumption that all who are exposed have immunity against further infection, it is unlikely that Mumbai would see a large third wave. It is the reinfections that may lead to a large wave. Reinfections could occur because of declining antibodies amongst the infected as well as by variants that can break through the immunity provided by prior infections. Even under a reasonably pessimistic scenario we observe the resulting peak to be no larger than that under the second wave. We further observe that under the scenario where the reinfections are mild so that they affect the fatality figures negligibly, where the new variants (beyond the existing delta variant) have a mild impact, as the city opens up, we observe a small wave in the coming months. However, if by then the vaccine coverage is extensive, this wave will be barely noticeable. We also plot $R_t$, the infection growth rate at time $t$, and highlight some interesting observations.
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India has been hit by a huge second wave of Covid-19 that started in mid-February 2021. Mumbai was amongst the first cities to see the increase. In this report, we use our agent based simulator to computationally study the second wave in Mumbai. We build upon our earlier analysis, where projections were made from November 2020 onwards. We use our simulator to conduct an extensive scenario analysis - we play out many plausible scenarios through varying economic activity, reinfection levels, population compliance, infectiveness, prevalence and lethality of the possible variant strains, and infection spread via local trains to arrive at those that may better explain the second wave fatality numbers. We observe and highlight that timings of peak and valley of the fatalities in the second wave are robust to many plausible scenarios, suggesting that they are likely to be accurate projections for Mumbai. During the second wave, the observed fatalities were low in February and mid-March and saw a phase change or a steep increase in the growth rate after around late March. We conduct extensive experiments to replicate this observed sharp convexity. This is not an easy phenomena to replicate, and we find that explanations such as increased laxity in the population, increased reinfections, increased intensity of infections in Mumbai transportation, increased lethality in the virus, or a combination amongst them, generally do a poor job of matching this pattern. We find that the most likely explanation is presence of small amount of extremely infective variant on February 1 that grows rapidly thereafter and becomes a dominant strain by Mid-March. From a prescriptive view, this points to an urgent need for extensive and continuous genome sequencing to establish existence and prevalence of different virus strains in Mumbai and in India, as they evolve over time.
The nation-wide lockdown starting 25 March 2020, aimed at suppressing the spread of the COVID-19 disease, was extended until 31 May 2020 in three subsequent orders by the Government of India. The extended lockdown has had significant social and economic consequences and `lockdown fatigue has likely set in. Phased reopening began from 01 June 2020 onwards. Mumbai, one of the most crowded cities in the world, has witnessed both the largest number of cases and deaths among all the cities in India (41986 positive cases and 1368 deaths as of 02 June 2020). Many tough decisions are going to be made on re-opening in the next few days. In an earlier IISc-TIFR Report, we presented an agent-based city-scale simulator(ABCS) to model the progression and spread of the infection in large metropolises like Mumbai and Bengaluru. As discussed in IISc-TIFR Report 1, ABCS is a useful tool to model interactions of city residents at an individual level and to capture the impact of non-pharmaceutical interventions on the infection spread. In this report we focus on Mumbai. Using our simulator, we consider some plausible scenarios for phased emergence of Mumbai from the lockdown, 01 June 2020 onwards. These include phased and gradual opening of the industry, partial opening of public transportation (modelling of infection spread in suburban trains), impact of containment zones on controlling infections, and the role of compliance with respect to various intervention measures including use of masks, case isolation, home quarantine, etc. The main takeaway of our simulation results is that a phased opening of workplaces, say at a conservative attendance level of 20 to 33%, is a good way to restart economic activity while ensuring that the citys medical care capacity remains adequate to handle the possible rise in the number of COVID-19 patients in June and July.
A second wave of SARS-CoV-2 is unfolding in dozens of countries. However, this second wave manifests itself strongly in new reported cases, but less in death counts compared to the first wave. Over the past three months in Germany, the reported cases increased by a factor five or more, whereas the death counts hardly grew. This discrepancy fueled speculations that the rise of reported cases would not reflect a second wave but only wider testing. We find that this apparent discrepancy can be explained to a large extent by the age structure of the infected, and predict a pronounced increase of death counts in the near future, as the spread once again expands into older age groups. To re-establish control, and to avoid the tipping point when TTI capacity is exceeded, case numbers have to be lowered. Otherwise the control of the spread and the protection of vulnerable people will require more restrictive measures latest when the hospital capacity is reached.
The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.
Vaccination against COVID-19 with the recently approved mRNA vaccines BNT162b2 (BioNTech/Pfizer) and mRNA-1273 (Moderna) is currently underway in a large number of countries. However, high incidence rates and rapidly spreading SARS-CoV-2 variants are concerning. In combination with acute supply deficits in Europe in early 2021, the question arises of whether stretching the vaccine, for instance by delaying the second dose, can make a significant contribution to preventing deaths, despite associated risks such as lower vaccine efficacy, the potential emergence of escape mutants, enhancement, waning immunity, reduced social acceptance of off-label vaccination, and liability shifts. A quantitative epidemiological assessment of risks and benefits of non-standard vaccination protocols remains elusive. To clarify the situation and to provide a quantitative epidemiological foundation we develop a stochastic epidemiological model that integrates specific vaccine rollout protocols into a risk-group structured infectious disease dynamical model. Using the situation and conditions in Germany as a reference system, we show that delaying the second vaccine dose is expected to prevent deaths in the four to five digit range, should the incidence resurge. We show that this considerable public health benefit relies on the fact that both mRNA vaccines provide substantial protection against severe COVID-19 and death beginning 12 to 14 days after the first dose. The benefits of protocol change are attenuated should vaccine compliance decrease substantially. To quantify the impact of protocol change on vaccination adherence we performed a large-scale online survey. We find that, in Germany, changing vaccination protocols may lead to small reductions in vaccination intention. In sum, we therefore expect the benefits of a strategy change to remain substantial and stable.
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