By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model’s predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved SARS-CoV-2 infections during its initial invasion of the US remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the US through March 12, we estimated that 22,876 (95% posterior predictive interval: 7,451 - 53,044) infections occurred in the US by this date. By comparing the model's predictions of symptomatic infections to local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between February 21 and March 12, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.97, 95% PPI: 0.85 - 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the US.

Importance: In the United States, schools closed in March 2020 to reduce the burden of COVID-19. They are now reopening amid high incidence in many places, necessitating analyses of the associated risks and benefits. Objective: To determine the impact of school reopening with varying levels of operating capacity and face-mask adherence on COVID-19 burden. Design: Modeling study using an agent-based model that simulates daily activities of the population. Transmission can occur in places such as schools, workplaces, community, and households. Model parameters were calibrated to and validated against multiple types of COVID-19 data. Setting: Indiana, United States of America. Participants: Synthetic population of Indiana. K-12 students, teachers, their families, and others in the state were studied separately. Interventions: Reopening of schools under three levels of school operating capacity (50%, 75%, and 100%), as well as three assumptions about face-mask adherence in schools (50%, 75%, and 100%). We compared the impact of these scenarios to reopening at full capacity without face masks and a scenario with schools operating remotely, for a total of 11 scenarios. Main outcomes: SARS-CoV-2 infections, symptomatic cases, and deaths due to COVID-19 from August 24 to December 31. Results: We projected 19,527 (95% CrI: 4,641-56,502) infections and 360 (95% CrI: 67-967) deaths in the scenario where schools operated remotely from August 24 to December 31. Reopening at full capacity with low face-mask adherence in schools resulted in a proportional increase of 81.7 (95% CrI: 78.2-85.3) times the number of infections and 13.4 (95% CrI: 12.8-14.0) times the number of deaths. High face-mask adherence resulted in a proportional increase of 3.0 (95% CrI: 2.8-3.1) times the number of infections. Operating at reduced capacity with high face-mask adherence resulted in only an 11.6% (95% CrI: 5.50%-17.9%) increase in the number of infections. Conclusions and Relevance: Reduced capacity and high face-mask adherence in schools would substantially reduce the burden of COVID-19 in schools and across the state. We did not explore the impact of other reopening scenarios, such as alternating days of attendance. Heterogeneous decisions could be made across different districts throughout the state, which our model does not capture. Hence, caution should be taken in interpreting our results as specific quantitative targets for operating capacity or face-mask adherence. Rather, our results suggest that schools should give serious consideration to reducing capacity as much as is feasible and enforcing adherence to wearing face masks.

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Though instances of arthropod-borne (arbo)virus co-infection have been documented clinically, the overall incidence of arbovirus co-infection and its drivers are not well understood. Now that dengue, Zika, and chikungunya viruses are all in circulation across tropical and subtropical regions of the Americas, it is important to understand the environmental and biological conditions that make co-infections more likely to occur. To understand this, we developed a mathematical model of cocirculation of two arboviruses, with transmission parameters approximating dengue, Zika, and/or chikungunya viruses and co-infection possible in both humans and mosquitoes. We examined the influence of seasonal timing of arbovirus cocirculation on the extent of co-infection. By undertaking a sensitivity analysis of this model, we examined how biological factors interact with seasonality to determine arbovirus co-infection transmission and prevalence. We found that temporal synchrony of the co-infecting viruses and average temperature were the most influential drivers of co-infection incidence. For seasonal patterns typical of a tropical region, we observed non-negligible incidence irrespective of arrival time when two arboviruses arrived simultaneously. Under our default parameter settings, this corresponded to a maximum co-infection cumulative incidence of 83 per 1,000 individuals and a minimum cumulative incidence of 32 per 1,000 individuals in the year following arrival. For seasonal patterns typical of a more temperate region, co-infections only occurred if arrivals took place near the seasonal peak, and even then, did not reach 0.01 co-infections per 1,000 individuals. Our model highlights the synergistic effect of co-transmission from mosquitoes, which leads to more than double the number of co-infections than would be expected in a scenario without co-transmission. Our results show that arbovirus co-infections are unlikely to occur in appreciable numbers unless epidemics overlap in space and time and in a tropical region.