One contribution of 15 to a theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.Subject Areas: computational biology, health and disease and epidemiology Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks-the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014-2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country-and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk (N eff ) and initial number of exposed individuals (E 0 ) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-ofinfection (t f/2 ) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R 0 1.7 from country-level data appears to seriously underestimate R 0 3.3 2 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate t f/2 22 weeks, compared with 8-10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4-4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature.This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities.

Background Coccidioidomycosis is an emerging infection in the southwestern United States. We examined the effects of precipitation and temperature on the incidence of coccidioidomycosis in California during 2000-2020, and estimated incident cases attributable to the California droughts of 2007-09 and 2012-15. Methods We analyzed monthly California coccidioidomycosis surveillance data from 2000-2020 at the census tract-level using generalized additive models. Models included distributed lags of precipitation and temperature within each endemic county, pooled using fixed-effects meta-analysis. An ensemble prediction algorithm of incident cases per census tract was developed to estimate the impact of drought on expected cases. Results Across 14 counties examined, coccidioidomycosis was strongly suppressed during, and amplified following, the 2007-2009 and 2012-2015 droughts. An estimated excess of 1,358 and 2,461 drought-attributable cases were observed in California in the two years following the 2007-2009 and 2012-2015 droughts, respectively. These post-drought excess cases more than offset the drought-attributable declines of 1,126 and 2,192 cases, respectively, that occurred during the 2007-2009 and 2012-2015 droughts. Across counties, a temperature increase from the 25th to 75th percentile (interquartile range) in the summer was associated with a doubling of incidence in the following fall (incidence rate ratio (IRR): 2.02, 95% CI: 1.84, 2.22), and a one IQR increase in precipitation in the winter was associated with 1.45 (95% CI: 1.36, 1.55) times higher incidence in the fall. The effect of winter precipitation was stronger (interaction coefficient representing ratio of IRRs: 1.36, 95% CI: 1.25, 1.48) when preceded by two dry rather than average winters. Incidence in arid lower San Joaquin Valley counties was most sensitive to winter precipitation fluctuations, while incidence in wetter coastal counties was most sensitive to summer temperature fluctuations. Conclusions In California, wet winters along with hot summers, particularly those following previous dry years, increased risk of coccidioidomycosis in California. Drought conditions may suppress incidence, then amplify incidence in subsequent years. With anticipated increasing frequency of drought in California, continued expansion of incidence, particularly in wetter, coastal regions, is expected.

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