Since late 2015 there has been an outbreak of Zika virus disease in South and Central America. This is particularly alarming because of its connection to microcephaly in infants born to mothers who were infected while pregnant. There are two mass gatherings which happened in 2016 in Brazil: Carnival (February 6-10 2016) and the Olympics (August 5-21, 2016). These events brought large groups of foreigners to Brazil who could have been exposed to the virus and transported it back to their home country. We created a mathematical model to analyze if a group of visitors to Rio de Janeiro for either of these events would cause an outbreak in Miami, Florida when they went home. Our model shows that if conditions are assumed to be the same for the populations in Miami and Rio de Janeiro, the visitors to Carnival could cause an outbreak in Miami in October that in three months infects roughly 75% of the population. If, however, the parameters for the model are modified to reflect different lifestyles and mosquito populations in Miami, the size of the outbreak there can be reduced. The model for Rio de Janeiro suggests that by August the majority of the population will already have been infected, hence immune, and there will be a low number of mosquitoes. Therefore, our model predicts that due to reduced infection rates during the Olympics the chance of visitors bringing back the disease to Miami is very low. 435

BACKGROUND During the peak of the winter 2020-21 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231 and the majority of these outbreaks were in high-priority settings. Local health jurisdictions (LHJs), which were primarily responsible for case and outbreak investigations, were overly burdened. Systematic cluster detection using real-time surveillance data could reduce this burden. OBJECTIVE To improve outbreak detection, the Washington State Department of Health initiated a systematic statewide cluster detection model to identify timely and actionable COVID-19 clusters for investigation and resource prioritization. This report details the implementation of the model using SaTScan, along with an assessment of the tool’s effectiveness. METHODS Six LHJs participated in a pilot before statewide implementation in August 2021. Clusters during July 17–December 17, 2021 were analyzed by LHJ population size and incidence. Clusters were matched to reported outbreaks and compared by setting RESULTS A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters. The median cluster size was 15 cases and the median number of clusters was 4. Nearly 60% of clusters were timely (ending within one week before the analysis). There were 2874 reported outbreaks during this same time period; 363 (12.8%) matched to ≥1 cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (28.7%, 29.8%), workplaces (21.5%, 15.4%), and long-term care facilities (18.8%, 27.3%). Settings with the highest percentage matching were community settings (22.2%) and congregate housing (20.8%). Approximately one-third (32.8%) of matched outbreaks had all cases linked after the cluster was identified. CONCLUSIONS Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters throughout the state. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, successfully meeting the goals. Among some high priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters which were matched to reported outbreaks. In workplaces, another high priority setting, results suggest the SaTScan model might be able to identify outbreaks sooner than existing outbreak detection methods.