Modelling how face masks and symptoms-based quarantine synergistically and cost-effectively reduce SARS-CoV-2 transmission in Bangladesh.

BACKGROUND: Non-pharmaceutical interventions (NPIs) used to limit SARS-CoV-2 transmission vary in their feasibility, appropriateness and effectiveness in different contexts. In Bangladesh a national lockdown implemented in March 2020 exacerbated poverty and was untenable long-term. A resurgence in 2021 warranted renewed NPIs. We sought to identify NPIs that were feasible in this context and explore potential synergies between interventions. METHODS: We developed an SEIR model for Dhaka District, parameterised from literature values and calibrated to data from Bangladesh. We discussed scenarios and parameterisations with policymakers with the aid of an interactive app. These discussions guided modelling of lockdown and two post-lockdown measures considered feasible to deliver; symptoms-based household quarantining and compulsory mask-wearing. We compared NPI scenarios on deaths, hospitalisations relative to capacity, working days lost, and cost-effectiveness. RESULTS: Lockdowns alone were predicted to delay the first epidemic peak but could not prevent overwhelming of the health service and were costly in lost working days. Impacts of post-lockdown interventions depended heavily on compliance. Assuming 80% compliance, symptoms-based household quarantining alone could not prevent hospitalisations exceeding capacity, whilst mask-wearing prevented overwhelming health services and was cost-effective given masks of high filtration efficiency. Combining masks with quarantine increased their impact. Recalibration to surging cases in 2021 suggested potential for a further wave in 2021, dependent on uncertainties in case reporting and immunity. CONCLUSIONS: Masks and symptoms-based household quarantining synergistically prevent transmission, and are cost-effective in Bangladesh. Our interactive app was valuable in supporting decision-making, with mask-wearing being mandated early, and community teams being deployed to support quarantining across Dhaka. These measures likely contributed to averting the worst public health impacts, but delivering an effective response with consistent compliance across the population has been challenging. In the event of a further resurgence, concurrent messaging to increase compliance with both mask-wearing and quarantine is recommended.

Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile.

The COVID-19 pandemic has affected cities particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. Our analyses show a strong association between socioeconomic status and both COVID-19 outcomes and public health capacity. People living in municipalities with low socioeconomic status did not reduce their mobility during lockdowns as much as those in more affluent municipalities. Testing volumes may have been insufficient early in the pandemic in those places, and both test positivity rates and testing delays were much higher. We find a strong association between socioeconomic status and mortality, measured by either COVID-19-attributed deaths or excess deaths. Finally, we show that infection fatality rates in young people are higher in low-income municipalities. Together, these results highlight the critical consequences of socioeconomic inequalities on health outcomes.

Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile.

The current coronavirus disease 2019 (COVID-19) pandemic has impacted dense urban populations particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality patterns, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. We find that among all age groups, there is a strong association between socioeconomic status and both mortality -measured either by direct COVID-19 attributed deaths or excess deaths- and public health capacity. Specifically, we show that behavioral factors like human mobility, as well as health system factors such as testing volumes, testing delays, and test positivity rates are associated with disease outcomes. These robust patterns suggest multiple possibly interacting pathways that can explain the observed disease burden and mortality differentials: (i) in lower socioeconomic status municipalities, human mobility was not reduced as much as in more affluent municipalities; (ii) testing volumes in these locations were insufficient early in the pandemic and public health interventions were applied too late to be effective; (iii) test positivity and testing delays were much higher in less affluent municipalities, indicating an impaired capacity of the health-care system to contain the spread of the epidemic; and (iv) infection fatality rates appear much higher in the lower end of the socioeconomic spectrum. Together, these findings highlight the exacerbated consequences of health-care inequalities in a large city of the developing world, and provide practical methodological approaches useful for characterizing COVID-19 burden and mortality in other segregated urban centers.

Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.

Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.In April 2019, only six weeks after Cyclone Idai devastated Mozambique's central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth's landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.The model prior to Kenneth's arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model's utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.