Managing COVID-19 Pandemic across Geography and Demography

This page provides resources, original research, and tools that could help individuals and policy makers with a scientific, balanced, and evidence-based approach to manage and navigate the COVID-19 Pandemic across the globe.

This dashboard provides simulations of COVID mortality in US counties based on a physical modelling of the pandemic using historical data, climate, the county geographic and demographic information, and its social mobility. The methodology and model are described in our article, below. It can be used to predict the impact of local policies on lockdown, social distancing, and face masks, on future of mortality, which can inform policies customized for each community.

Diverse local epidemics reveal the distinct effects of population density, demographics, climate, depletion of susceptibles, and intervention in the first wave of COVID-19 in the United States

From our paper: This figure shows the phase portrait of COVID-19 epidemic in NYC, plotting Daily vs Total Mortality per population. The green disc shows the “herd immunity threshold” which is the fixed point of the epidemic, at normal social mobility. The red curves are predicted trajectories at normal mobility, while the blue curves are for -42% mobility. The black curve shows the 7-day rolling average of the reported mortality.

Abstract of our paper with Benjamin Holder, Daniel Lichtblau, and Mads Bahrami: The SARS-CoV-2 pandemic has caused significant mortality and morbidity worldwide, sparing almost no community. As the disease will likely remain a threat for years to come, an understanding the precise influences of human demographics and settlement, and the dynamic factors of climate and intervention, on the spread of localized epidemics will be vital for mounting an effective response. We consider the entire set of local epidemics in the United States; a broad selection of demographic, population density, and climate factors; and local mobility data, tracking social distancing interventions, to determine the key factors driving the spread and containment of the virus. Assuming first a linear model for the rate of exponential growth (or decay) in cases/mortality, we find that population-weighted density, humidity, and median age dominate the dynamics of growth and decline, once interventions are accounted for. A focus on distinct metropolitan areas suggests that some locales benefited from the timing of a nearly simultaneous nationwide shutdown, and/or the regional climate conditions in mid-March; while others suffered significant outbreaks prior to intervention, which were in some cases amplified by low-humidity conditions. Then, using a first-principles model of the infection spread, we develop predictions for the impact of the relaxation of social distancing and local climate conditions. A few regions, where a significant fraction of the population was infected, show evidence that the epidemic has partially resolved via depletion of the susceptible population (i.e., “herd immunity”), while most regions in the United States remain overwhelmingly susceptible. The model also allows for predictions of age-dependent incubation periods and infection probability. These results will be important for optimal management of intervention strategies, which can be facilitated using our online dashboard.

United States COVID Immunity Maps (as of June 22nd, 2020)

These maps show the immunity, and/or rate of relative growth of COVID mortality in different US counties, should social mobility returns to its normal (pre-Pandemic) level:

Counties in cyan have passed the immunity threshold, while those in blue are within 1-sigma threshold of immunity
Predicted relative mortality growth rate (in units of 1/day) for US counties, in lieu of social distancing or other interventions
Significance (growth rate divided by its error), or confidence in positive COVID growth across the US, at normal social mobility
Predicted Relative daily mortality growth rate across the US, at normal social mobility