Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model

We propose a modified population-based susceptible-exposed-infectious-recovered (SEIR) compartmental model for a retrospective study of the COVID-19 transmission dynamics in India during the first wave. We extend the conventional SEIR methodology to account for the complexities of COVID-19 infection, its multiple symptoms, and transmission pathways. In particular, we consider a time-dependent transmission rate to account for governmental controls (e.g., national lockdown) and individual behavioral factors (e.g., social distancing, mask-wearing, personal hygiene, and self-quarantine). An essential feature of COVID-19 that is different from other infections is the significant contribution of asymptomatic and pre-symptomatic cases to the transmission cycle. A Bayesian method is used to calibrate the proposed SEIR model using publicly available data (daily new tested positive, death, and recovery cases) from several Indian states. The uncertainty of the parameters is naturally expressed as the posterior probability distribution. The calibrated model is used to estimate undetected cases and study different initial intervention policies, screening rates, and public behavior factors, that can potentially strike a balance between disease control and the humanitarian crisis caused by a sudden strict lockdown.

Individual-based modeling of COVID-19 transmission in college communities.

The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.

SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study.

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1-85.7%) and 87% (CrI: 80.0-92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

Risk factors of SARS-CoV-2 infection in healthcare workers: a retrospective study of a nosocomial outbreak.

BACKGROUND: Healthcare workers (HCWs) are at the forefront of fighting against the COVID-19 pandemic. However, they are at high risk of acquiring the pathogen from infected patients and transmitting to other HCWs. We aimed to investigate risk factors for nosocomial COVID-19 infection among HCWs in a non-COVID-19 hospital yard. METHODS: Retrospective data collection on demographics, lifestyles, contact status with infected subjects for 118 HCWs (including 12 COVID-19 HCWs) at Union Hospital of Wuhan, China. Sleep quality and working pressure were evaluated by the Pittsburgh Sleep Quality Index (PSQI) and The Nurse Stress Index (NSI), respectively. The follow-up duration was from Dec 25, 2019, to Feb 15, 2020. RESULTS: A high proportion of COVID-19 HCWs had engaged in night shift-work (75.0% vs. 40.6%) and felt working under pressure (66.7% vs. 32.1%) than uninfected HCWs. SARS-CoV-2 infected HCWs had significantly higher scores of PSQI and NSI than uninfected HCWs (P < 0.001). Specifically, scores of 5 factors (sleep quality, sleep time, sleep efficiency, sleep disorder, and daytime dysfunction) in PSQI were higher among infected HCWs. For NSI, its 5 subscales (nursing profession and work, workload and time allocation, working environment and resources, patient care, management and interpersonal relations) were all higher in infected than uninfected nurse. Furthermore, total scores of PSQI (HR = 2.97, 95%CI = 1.86-4.76; P <0.001) and NSI (HR = 4.67, 95%CI = 1.42-15.45; P = 0.011) were both positively associated with the risk of SARS-CoV-2 infection. CONCLUSION: Our analysis shows that poor sleep quality and higher working pressure may increase the risk of nosocomial SARS-CoV-2 infection among HCWs.