The COVID-19 Incarceration Model: a tool for corrections staff to analyze outbreaks of COVID-19

Correctional facilities are at high risk of COVID-19 outbreaks due to the inevitable close contacts in the environment. Such facilities are a high priority in the public health response to the epidemic. We developed a user-friendly Excel spreadsheet model (building on the previously developed Recidiviz model) to analyze COVID-19 outbreaks in correctional facilities and the potential impact of prevention strategies -the COVID-19 Incarceration Model. The model requires limited inputs and can be used by non-modelers. The impact of a COVID-19 outbreak and mitigation strategies is illustrated for an example prison setting.

Understanding COVID-19 dynamics and the effects of interventions in the Philippines: A mathematical modelling study

ObjectiveCOVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. MethodsWe applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. FindingsPopulation age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-26%. The February 2021 case detection rate was estimated at [~]9%, population recovered at [~]12%, and scenario projections indicated high sensitivity to MHS adherence. ConclusionsCOVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS should allow the Philippines to maintain epidemic control until vaccines are widely distributed, but disease resurgence could occur due to low population immunity and detection rates.