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1. Epidemiological and Economic Impact of COVID-19 in the US

   Chen, J; Vullikanti, A; Santos, J; Venkatramanan, S; Hoops, S; Mortveit, H; Lewis, B; You, W; Eubank, S; Marathe, M; et al (November 2020, medRxiv)

   This research measures the epidemiological and economic impact of COVID-19 spread in the US under different mitigation scenarios, comprising of non-pharmaceutical interventions. A detailed disease model of COVID-19 is combined with a model of the US economy to estimate the direct impact of labor supply shock to each sector arising from morbidity, mortality, and lockdown, as well as the indirect impact caused by the interdependencies between sectors. During a lockdown, estimates of jobs that are workable from home in each sector are used to modify the shock to labor supply. Results show trade-os between economic losses, and lives saved and infections averted are non-linear in compliance to social distancing and the duration of lockdown. Sectors that are worst hit are not the labor-intensive sectors such as Agriculture and Construction, but the ones with high valued jobs such as Professional Services, even after the teleworkability of jobs is accounted for. Additionally, the findings show that a low compliance to interventions can be overcome by a longer shutdown period and vice versa to arrive at similar epidemiological impact but their net effect on economic loss depends on the interplay between the marginal gains from averting infections and deaths, versus the marginal loss from having healthy workers stay at home during the shutdown. 

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2. Policies for a Second Wave

   Baqaee, D.; Farhi, E.; Mina, M.; Stock, J. (July 2020, Brookings papers on economic activity)

   Eberly, Jan; Romer, David (Ed.)

   In the spring of 2020, the initial surge of COVID-19 infections and deaths was flattened using a combination of economic shutdowns and noneconomic non-pharmaceutical interventions (NPIs). The possibility of a second wave of infections and deaths raises the question of what interventions can be used to significantly reduce deaths while supporting, not preventing, economic recovery. We use a five-age epidemiological model combined with sixty-six-sector economic accounting to examine policies to avert and to respond to a second wave. We find that a second round of economic shutdowns alone are neither sufficient nor necessary to avert or quell a second wave. In contrast, noneconomic NPIs, such as wearing masks and personal distancing, increasing testing and quarantine, reintroducing restrictions on social and recreational gatherings, and enhancing protections for the elderly together can mitigate a second wave while leaving room for an economic recovery. 

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3. Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread

   https://doi.org/10.4081/jphr.2021.1906  

   Zhang, Hongzhe; Zhao, Xiaohang; Yin, Kexin; Yan, Yiren; Qian, Wei; Chen, Bintong; Fang, Xiao (January 2021, Journal of Public Health Research)

   null (Ed.)

   Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread. 

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4. Pandemic-associated mobility restrictions could cause increases in dengue virus transmission

   https://doi.org/10.1371/journal.pntd.0009603  

   Cavany, Sean M.; España, Guido; Vazquez-Prokopec, Gonzalo M.; Scott, Thomas W.; Perkins, T Alex (August 2021, PLOS Neglected Tropical Diseases)

   Christofferson, Rebecca C. (Ed.)

   Background The COVID-19 pandemic has induced unprecedented reductions in human mobility and social contacts throughout the world. Because dengue virus (DENV) transmission is strongly driven by human mobility, behavioral changes associated with the pandemic have been hypothesized to impact dengue incidence. By discouraging human contact, COVID-19 control measures have also disrupted dengue vector control interventions, the most effective of which require entry into homes. We sought to investigate how and why dengue incidence could differ under a lockdown scenario with a proportion of the population sheltered at home. Methodology & principal findings We used an agent-based model with a realistic treatment of human mobility and vector control. We found that a lockdown in which 70% of the population sheltered at home and which occurred in a season when a new serotype invaded could lead to a small average increase in cumulative DENV infections of up to 10%, depending on the time of year lockdown occurred. Lockdown had a more pronounced effect on the spatial distribution of DENV infections, with higher incidence under lockdown in regions with higher mosquito abundance. Transmission was also more focused in homes following lockdown. The proportion of people infected in their own home rose from 54% under normal conditions to 66% under lockdown, and the household secondary attack rate rose from 0.109 to 0.128, a 17% increase. When we considered that lockdown measures could disrupt regular, city-wide vector control campaigns, the increase in incidence was more pronounced than with lockdown alone, especially if lockdown occurred at the optimal time for vector control. Conclusions & significance Our results indicate that an unintended outcome of lockdown measures may be to adversely alter the epidemiology of dengue. This observation has important implications for an improved understanding of dengue epidemiology and effective application of dengue vector control. When coordinating public health responses during a syndemic, it is important to monitor multiple infections and understand that an intervention against one disease may exacerbate another. 

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5. Predictions, Role of Interventions and Effects of a Historic National Lockdown in India's Response to the the COVID-19 Pandemic: Data Science Call to Arms

   https://doi.org/10.1162/99608f92.60e08ed5  

   Ghosh, Parikshit; Baladandayuthapani, Veerabhadran; Banerjee, Mousumi; Mukherjee, Bhramar; Ray, Debashree; Salvatore, Maxwell; Bhattacharyya, Rupam; Wang, Lili; Du, Jiacong; Mohammed, Shariq; et al (June 2020, Harvard Data Science Review)

   With only 536 COVID-19 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25, 2020. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. In this article, we use a Bayesian extension of the susceptible-infected-removed (eSIR) model designed for intervention forecasting to study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 infections in India compared to other, less severe nonpharmaceutical interventions. We compare effects of hypothetical durations of lockdown on reducing the number of active and new infections. We find that the lockdown, if implemented correctly, can reduce the total number of cases in the short term, and buy India invaluable time to prepare its health care and disease-monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for increased benefit (as measured by reduction in the number of cases). A longer lockdown from 42–56 days is preferable to substantially ‘flatten the curve’ when compared to 21–28 days of lockdown. Our models focus solely on projecting the number of COVID-19 infections and thus inform policymakers about one aspect of this multifaceted decision-making problem. We conclude with a discussion on the pivotal role of increased testing, reliable and transparent data, proper uncertainty quantification, accurate interpretation of forecasting models, reproducible data science methods, and tools that can enable data-driven policymaking during a pandemic. Our software products are available at covind19.org. 

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