Background: Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system. Methods: We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon. Results: We estimated that 56% (95% credible interval 42%–63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760–149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700–75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario’s ICU capacity. Interpretation: Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological andeconomic respite for populations.

Background: Haiti is in the midst of a cholera epidemic. Surveillance data for formulating models of the epidemic are limited, but such models can aid understanding of epidemic processes and help define control strategies. Objective: To predict, by using a mathematical model, the sequence and timing of regional cholera epidemics in Haiti and explore the potential effects of disease-control strategies. Design: Compartmental mathematical model allowing person-to-person and waterborne transmission of cholera. Within- and between-region epidemic spread was modeled, with the latter dependent on population sizes and distance between regional centroids (a “gravity” model). Setting: Haiti, 2010 to 2011. Data Sources: Haitian hospitalization data, 2009 census data, literature-derived parameter values, and model calibration. Measurements: Dates of epidemic onset and hospitalizations. Results: The plausible range for cholera's basic reproductive number (R₀, defined as the number of secondary cases per primary case in a susceptible population without intervention) was 2.06 to 2.78. The order and timing of regional cholera outbreaks predicted by the gravity model were closely correlated with empirical observations. Analysis of changes in disease dynamics over time suggests that public health interventions have substantially affected this epidemic. A limited vaccine supply provided late in the epidemic was projected to have a modest effect. Limitations: Assumptions were simplified, which was necessary for modeling. Projections are based on the initial dynamics of the epidemic, which may change. Conclusion: Despite limited surveillance data from the cholera epidemic in Haiti, a model simulating between-region disease transmission according to population and distance closely reproduces reported disease patterns. This model is a tool that planners, policymakers, and medical personnel seeking to manage the epidemic could use immediately. Primary Funding Source: None.

The ongoing economic and political crisis in Venezuela has resulted in a collapse of the healthcare system and the re-emergence of previously controlled or eliminated infectious diseases. There has also been an exodus of Venezuelan international migrants in response to the crisis. We sought to describe the infectious disease risks faced by Venezuelan nationals and assess the international mobility patterns of the migrant population. We synthesized data on recent infectious disease events in Venezuela and among international migrants from Venezuela, as well as on current country of residence among the migrant population. We used passenger-level itinerary data from the International Air Transport Association to evaluate trends in outbound air travel from Venezuela over time. We used two parameter-free mobility models, the radiation and impedance models, to estimate the expected population flows from Venezuelan cities to other major Latin American and Caribbean cities. Outbreaks of measles, diphtheria and malaria have been reported across Venezuela and other diseases, such as HIV and tuberculosis, are resurgent. Changes in migration in response to the crisis are apparent, with an increase in Venezuelan nationals living abroad, despite an overall decline in the number of outbound air passengers. The two models predicted different mobility patterns, but both highlighted the importance of Colombian cities as destinations for migrants and also showed that some migrants are expected to travel large distances. Despite the large distances that migrants may travel internationally, outbreaks associated with Venezuelan migrants have occurred primarily in countries proximate to Venezuela. Understanding where international migrants are relocating is critical, given the association between human mobility and the spread of infectious diseases. In data-limited situations, simple models can be useful for providing insights into population mobility and may help identify areas likely to receive a large number of migrants.

Background The period from February to June 2021 was one during which initial wild-type SARS-CoV-2 strains were supplanted in Ontario, Canada, first by variants of concern (VOC) with the N501Y mutation (Alpha/B1.1.17, Beta/B.1.351 and Gamma/P.1 variants), and then by the Delta/B.1.617 variant. The increased transmissibility of these VOCs has been documented but data for increased virulence is limited. We used Ontario’s COVID-19 case data to evaluate the virulence of these VOCs compared to non-VOC SARS-CoV-2 infections, as measured by risk of hospitalization, intensive care unit (ICU) admission, and death. Methods We created a retrospective cohort of people in Ontario testing positive for SARS-CoV-2 and screened for VOCs, with dates of test report between February 7 and June 22, 2021 (n=211,197). We constructed mixed effects logistic regression models with hospitalization, ICU admission, and death as outcome variables. Models were adjusted for age, sex, time, comorbidities, and pregnancy status. Health units were included as random intercepts. Results Compared to non-VOC SARS-CoV-2 strains, the adjusted elevation in risk associated with N501Y-positive variants was 59% (49-69%) for hospitalization; 105% (82-134%) for ICU admission; and 61% (40-87%) for death. Increases with Delta variant were more pronounced: 120% (93-153%) for hospitalization; 287% (198-399%) for ICU admission; and 137% (50-230%) for death. Interpretation The progressive increase in transmissibility and virulence of SARS-CoV-2 VOCs will result in a significantly larger, and more deadly, pandemic than would have occurred in the absence of VOC emergence.