ABSTRACT
Mexico has experienced one of the highest COVID-19 death rates in the world. A delayed response towards implementation of social distancing interventions until late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. Here, we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatial-temporal transmission patterns. The early estimates of reproduction number for Mexico were estimated between R[~]1.1-from genomic and case incidence data. Moreover, the mean estimate of R has fluctuated [~]1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories. We found that the sequential mortality forecasts from the GLM and Richards model predict downward trends in the number of deaths for all thirteen forecasts periods for Mexico and Mexico City. The sub-epidemic and IHME models predict more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21 - 09/28-10/27) for Mexico and Mexico City. Our findings support the view that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.
ABSTRACT
After weeks under lockdown, metropolitan areas fighting the spread of COVID-19 aim to balance public health goals with social and economic standards for well-being. Mathematical models of disease transmission seeking to evaluate mitigation strategies must assess the possible impacts of social distancing, economic lockdowns and other measures. However, obscure relations between model parameters and real-world phenomena complicate such analyses. Here, we use a high-resolution metapopulation model of Guadalajara (GDL, Western Mexico) to represent daily mobility patterns driven by economic activities and their relation to epidemic growth. Given the prominence of essential activities in the citys economy, we find that strategies aiming to mitigate the risk of out-of-home interactions are insufficient to stop the catastrophic spread of COVID-19. Using baseline reproduction numbers R0 = [2.5, 3.0] in the absence of interventions, our simulations suggest that household transmission alone can make Rt [~] 1, and is estimated to drive 70 {+/-}15% of current epidemic growth. This sets an upper bound for the impact of mobility-based interventions, which are unlikely to lower Rt below 1.3 and must be complemented with aggressive campaigns for early case detection and isolation. As laboratory testing and health services become insufficient to meet demand in GDL and most other cities, we propose that cities facilitate guidelines and equipment to help people curb spreading within their own homes. Postponing these actions will increase their economic cost and decrease their potential returns. Author summaryPublic health strategies to mitigate the spread of COVID-19 in metropolitan areas have focused on preventing transmission in schools, work sites and other public spaces. Here, we use a demographically- and spatially-explicit model of Guadalajara (GDL, Western Mexico) to represent economic lockdowns and their impact on disease spread. Our findings suggest that viral exposure within households accounts for 70{+/-}15% of the epidemics current growth rate. This highlights the importance of early case detection and isolation as necessary measures to prevent the spread of COVID-19 between strangers and close contacts alike.
