Application of disease model to ascertain risk-mitigation strategies during COVID-19 pandemic in 2020, an example from resource-constrained urban settings in Pakistan

Abstract Most COVID-19 affected resource-constrained settings have not ascertained their disease prevalence that pose a risk for global health. In wake of limited diagnostics and research capacity in such settings, this disease forecasting model provides an example to be adapted for evidence-based response efforts. Using officially reported data, this model forecasted COVID-19 prevalence in cosmopolitan cities. Several risk-mitigation strategies were analyzed for effectiveness in controlling disease incidence. Moreover, the reproduction rates to ascertain transmission, herd-immunity threshold, and performance of required laboratory tests were studied. The severe-critical cases were relatively low due to larger young population. Following any risk-mitigation strategy, at end of first wave, a susceptible population remained at risk of recurrent COVID-19 transmission. The herd-immunity threshold was in accordance with global estimates but would need careful monitoring based on adopted risk-mitigation strategy and variation in vaccines’ efficacy. A test-gap between performed vs required laboratory tests led to miss several cases from getting diagnosed. First of its kind, this study estimates sub-national COVID-19 prevalence in dense-urban living in low-middle income settings. Future response policies should consider such evidence to prevent recurrent COVID-19 waves of transmission. Unless sustained herd-immunity is achieved by effective immunization, risk of re-introduction to vulnerable population would remain.