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The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.

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The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time Epidemic Type Aftershock Sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (UK). We investigate the extent to which i) ETAS models of short term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatment and control hotspots throughout the experimental period, we use a novel experimental design to * Department of Mathematics and Computer Science, Santa Clara University † School of Mathematics, Georgia Institute of Technology ‡ Los Angeles Police Department § Kent Police Service ¶ Department of Criminology, University of California, Irvine Department of Mathematics, University of California, Los Angeles * * Department of Anthropology, University of California, Los Angeles 1 allow treatment and control hotspots to change dynamically over the course of the experiment. Our results show that ETAS models predict 1.4-2.2 times as much crime compared to a dedicated crime analyst using existing criminal intelligence and hotspot mapping practice. Police patrols using ETAS forecasts led to a average 7.4% reduction in crime volume as a function of patrol time, whereas patrols based upon analyst predictions showed no significant effect. Dynamic police patrol in response to ETAS crime forecasts can disrupt opportunities for crime and lead to real crime reductions.

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We described the change in drug overdoses during the COVID-19 pandemic in one urban emergency medical services (EMS) system. Data was collected from Marion County, Indiana (Indianapolis), including EMS calls for service (CFS) for suspected overdose, CFS in which naloxone was administered, and fatal overdose data from the County Coroner's Office. With two sample t tests and ARIMA time series forecasting, we showed changes in the daily rates of calls (all EMS CFS, overdose CFS, and CFS in which naloxone was administered) before and after the stay-at-home order in Indianapolis. We further showed differences in the weekly rate of overdose deaths. Overdose CFS and EMS naloxone administration showed an increase with the social isolation of the Indiana stay-at-home order, but a continued increase after the stay-at-home order was terminated. Despite a mild 4% increase in all EMS CFS, overdose CFS increased 43% and CFS with naloxone administration increased 61% after the stay-athome order. Deaths from drug overdoses increased by 47%. There was no change in distribution of age, race/ethnicity, or zip code of those who overdosed after the stay-at-home order was issued. We hope this data informs policy-makers preparing for future COVID-19 responses and other disaster responses.

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Our goal in this paper is to analyze temporal patterns of civilian death reports in Iraq. For this purpose we employ a branching point process model similar to those used in earthquake analysis. Here the rate of events is partitioned into the sum of a Poisson background rate and a self-exciting component in which events trigger an increase in the rate of the process. More specifically, each event generated by the process in turn generates a sequence of offspring Our results indicate that branching point processes are well suited for modeling the temporal dynamics of violence in Iraq.

While the presence of clustering in crime and security event data is well established, the mechanism(s) by which clustering arises is not fully understood. Both contagion models and history independent correlation models are applied, but not simultaneously. In an attempt to disentangle contagion from other types of correlation, we consider a Hawkes process with background rate driven by a log Gaussian Cox process. Our inference methodology is an efficient Metropolis adjusted Langevin algorithm for filtering of the intensity and estimation of the model parameters. We apply the methodology to property and violent crime data from Chicago, terrorist attack data from Northern Ireland and Israel, and civilian casualty data from Iraq. For each data set we quantify the uncertainty in the levels of contagion vs. history independent correlation.