A population-representative serological study was conducted in all districts of the state of Tamil Nadu (population 72 million), India, in October-November 2020. State-level seroprevalence was 31.6%. However, this masks substantial variation across the state. Seroprevalence ranged from just 11.1% in The Nilgris to 51.0% in Perambalur district. Seroprevalence in urban areas (36.9%) was higher than in rural areas (26.9%). Females (30.8%) had similar seroprevalence to males (30.3%). However, working age populations (age 40-49: 31.6%) have significantly higher seroprevalence than the youth (age 18-29: 30.7%) or elderly (age 70+: 25.8%). Estimated seroprevalence implies that at least 22.6 million persons were infected by the end of November, roughly 36 times the number of confirmed cases. Estimated seroprevalence implies an infection fatality rate of 0.052%.

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The amount of rental housing in India has declined significantly over the years for various reasons, including the nature of the rent control laws. This paper assesses the impact of rent control for Mumbai, where it has created a shortfall in formal, affordable rental housing and contributed to distortions in the land market. The paper describes how "first-generation" rent control in Mumbai has led to deterioration of the existing rental housing stock, virtually halted the construction of new housing for rental in the city, and given rise to informal practices such as pagdi or key money. It also analyses the spatial concentration and composition of rent-controlled tenements in the city. It proposes reforms that would allow a gradual move towards rationalized rent controls, arguing that such second-generation controls will help incentivize investments in the rental sector and reduce the demand in the housing market at large, with implications for prices and affordable housing in particular.

Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible countercyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progression of the disease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease outbreak. We show via simulation of a stochastic Susceptible-Infected-Recovered (SIR) model and of a synthetic intervention (SI) model that adaptive control performs at least as well as immediate and full release from lockdown starting May 4 and as full release from lockdown after a month (i.e., after May 31). The key insight is that adaptive response provides the option to increase or decrease socioeconomic activity depending on how it affects disease progression and this freedom allows it to do at least as well as most other policy alternatives. We also discuss the central challenge to any nuanced release policy, including adaptive control, specifically learning how specific policies translate into changes in contact rates and thus COVID-19's reproductive rate in real time.

India has reported the fourth highest number of confirmed SARS-CoV-2 cases worldwide. Because there is little community testing for COVID, this case count is likely an underestimate. When India partially exited from lockdown on May 4, 2020, millions of daily laborers left cities for their rural family homes. RNA testing on a near-random sample of laborers returning to the state of Bihar is used to estimate positive testing rate for COVID across India for a 6-week period immediately following the initial lifting of India's lockdown. Positive testing rates among returning laborers are only moderately correlated with, and 21% higher than, Indian states' official reports, which are not based on random sampling. Higher prevalence among returning laborers may also reflect greater COVID spread in crowded poor communities such as slums.