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Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA.

ABSTRACT: In the United States, schools closed in March 2020 due to COVID-19 and began reopening in August 2020, despite continuing transmission of SARS-CoV-2. In states where in-person instruction resumed at that time, two major unknowns were the capacity at which schools would operate, which depended on the proportion of families opting for remote instruction, and adherence to face-mask requirements in schools, which depended on cooperation from students and enforcement by schools. To determine the impact of these conditions on the statewide burden of COVID-19 in Indiana, we used an agent-based model calibrated to and validated against multiple data types. Using this model, we quantified the burden of COVID-19 on K-12 students, teachers, their families, and the general population under alternative scenarios spanning three levels of school operating capacity (50 %, 75 %, and 100 %) and three levels of face-mask adherence in schools (50 %, 75 %, and 100 %). Under a scenario in which schools operated remotely, we projected 45,579 (95 % CrI: 14,109-132,546) infections and 790 (95 % CrI: 176-1680) deaths statewide between August 24 and December 31. Reopening at 100 % capacity with 50 % face-mask adherence in schools resulted in a proportional increase of 42.9 (95 % CrI: 41.3-44.3) and 9.2 (95 % CrI: 8.9-9.5) times that number of infections and deaths, respectively. In contrast, our results showed that at 50 % capacity with 100 % face-mask adherence, the number of infections and deaths were 22 % (95 % CrI: 16 %-28 %) and 11 % (95 % CrI: 5 %-18 %) higher than the scenario in which schools operated remotely. Within this range of possibilities, we found that high levels of school operating capacity (80-95 %) and intermediate levels of face-mask adherence (40-70 %) resulted in model behavior most consistent with observed data. Together, these results underscore the importance of precautions taken in schools for the benefit of their communities.

SUBMITTER: Espana G

PROVIDER: S-EPMC8366048 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21st, 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21st until May 4th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic.MethodsThis study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model.ResultsThe proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities.ConclusionsOur model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

Project description:BackgroundThe course of the COVID-19 pandemic has been driven by several dynamic behavioral, immunological, and viral factors. We used mathematical modeling to explore how the concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant affected the trajectory of the pandemic in India, using Andhra Pradesh (pop: 53 million) as an exemplar Indian state.MethodsWe constructed an age- and contact-structured compartmental model that allows for individuals to proceed through various states depending on whether they have received zero, one, or two doses of the COVID-19 vaccine. We calibrated our model using results from another model (ie, INDSCI-SIM) as well as available context-specific serosurvey data. The introduction of the Omicron variant is modelled alongside protection gained from hybrid immunity. We predict disease dynamics in the background of hybrid immunity coming from infections and an ongoing vaccination program, given prior levels of seropositivity from earlier waves of infection. We describe the consequences of school reopening on cases across different age-bands, as well as the impact of the Omicron (BA.2) variant.FindingsWe show the existence of an epidemic peak in India that is strongly related to the value of background seroprevalence. As expected, because children were not vaccinated in India, re-opening schools increases the number of cases in children more than in adults, although in all scenarios, the peak number of active hospitalizations was never greater than 0.45 times the corresponding peak in the Delta wave before schools were reopened. We varied the level of infection induced seropositivity in our model and found the height of the peak associated with schools reopening reduced as background infection-induced seropositivity increased from 20% to 40%. At reported values of seropositivity of 64% from representative surveys done in India, no discernable peak was observed. We also explored counterfactual scenarios regarding the effect of vaccination on hybrid immunity. We found that in the absence of vaccination, even at high levels of seroprevalence (>60%), the emergence of the Omicron variant would have resulted in a large rise in cases across all age bands by as much as 1.8 times. We conclude that the presence of high levels of hybrid immunity resulted in fewer cases in the Omicron wave than in the Delta wave.InterpretationIn India, decreasing prevalence of immunologically naïve individuals of all ages was associated with fewer cases reported once schools were reopened. In addition, hybrid immunity, together with the lower intrinsic severity of disease associated with the Omicron variant, contributed to low reported COVID-19 hospitalizations and deaths.FundingWorld Health Organization, Mphasis.

Project description:ImportanceSchool closures because of COVID-19 have left 1.6 billion students around the world without in-person classes for a prolonged period. To our knowledge, no study has documented whether reopening schools in low- and middle-income countries during the pandemic was associated with increased aggregate COVID-19 incidence and mortality with appropriate counterfactuals.ObjectiveTo test whether reopening schools under appropriate protocols during the COVID-19 pandemic was associated with increased municipal-level COVID-19 cases and deaths in São Paulo State, Brazil.Design setting and participantsThis observational study of municipalities in São Paulo State, Brazil, uses a difference-in-differences analysis to examine the association between municipal decisions to reopen schools during the COVID-19 pandemic and municipal-level COVID-19 case and death rates between October and December 2020. The study compared 129 municipalities that reopened schools in 2020 with 514 that did not and excluded data for 2 municipalities that reopened schools and closed then again.Main outcomes and measuresNew COVID-19 cases and deaths per 10 000 inhabitants up to 12 weeks after school reopenings and municipal-level aggregate mobility for a subset of municipalities.ResultsThere were 8764 schools in the 129 municipalities that reopened schools compared with 9997 in the control group of 514 municipalities that did not reopen schools. The municipalities that reopened schools had a cumulative COVID-19 incidence of 20 cases per 1000 inhabitants and mortality of 0.5 deaths per 1000 inhabitants in September 2020 (the baseline period) compared with an incidence of 18 cases per 1000 inhabitants and mortality of 0.45 deaths per 1000 inhabitants during the baseline period in the comparison group. The findings indicated that there were no statistically significant differences between municipalities that authorized schools to reopen and those that did not for (1) weekly new cases (difference-in-differences, -0.03; 95% CI, -0.09 to 0.03) and (2) weekly new deaths (difference-in-differences, -0.003; 95% CI, -0.011 to 0.004) before and after October 2020. Reopening schools was not associated with higher disease activity, even in relatively vulnerable municipalities, nor aggregate mobility.Conclusions and relevanceThe findings from this study suggest that keeping schools open during the COVID-19 pandemic did not contribute to the aggregate disease activity.

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Impacts of K-12 school reopening on the COVID-19 epidemic in Indiana, USA.

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