Surveillance for Harmful Algal Bloom Events and Associated Human and Animal Illnesses - One Health Harmful Algal Bloom System, United States, 2016-2018.

Harmful algal bloom events can result from the rapid growth, or bloom, of photosynthesizing organisms in natural bodies of fresh, brackish, and salt water. These events can be exacerbated by nutrient pollution (e.g., phosphorus) and warming waters and other climate change effects (1); have a negative impact on the health of humans, animals, and the environment; and damage local economies (2,3). U.S. harmful algal bloom events of public health concern are centered on a subset of phytoplankton: diatoms, dinoflagellates, and cyanobacteria (also called blue-green algae). CDC launched the One Health Harmful Algal Bloom System (OHHABS) in 2016 to inform efforts to prevent human and animal illnesses associated with harmful algal bloom events. A total of 18 states reported 421 harmful algal bloom events, 389 cases of human illness, and 413 cases of animal illness that occurred during 2016-2018. The majority of harmful algal bloom events occurred during May-October (413; 98%) and in freshwater bodies (377; 90%). Human and animal illnesses primarily occurred during June-September (378; 98%) and May-September (410; 100%). Gastrointestinal or generalized illness signs or symptoms were the most frequently reported (>40% of human cases and >50% of animal cases); however, multiple other signs and symptoms were reported. Surveillance data from harmful algal bloom events, exposures, and health effects provide a systematic description of these occurrences and can be used to inform control and prevention of harmful algal bloom-associated illnesses.

Defining County-Level Terrestrial Rabies Freedom Using the US National Rabies Surveillance System: Surveillance Data Analysis.

BACKGROUND: Rabies is a deadly zoonotic disease with nearly 100% fatality rate. In the United States, rabies virus persists in wildlife reservoirs, with occasional spillover into humans and domestic animals. The distribution of reservoir hosts in US counties plays an important role in public health decision-making, including the recommendation of lifesaving postexposure prophylaxis upon suspected rabies exposures. Furthermore, in surveillance data, it is difficult to discern whether counties have no cases reported because rabies was not present or because counties have an unreported rabies presence. These epizootics are monitored by the National Rabies Surveillance System (NRSS), to which approximately 130 state public health, agriculture, and academic laboratories report animal rabies testing statistics. Historically, the NRSS classifies US counties as free from terrestrial rabies if, over the previous 5 years, they and any adjacent counties did not report any rabies cases and they tested ≥15 reservoir animals or 30 domestic animals. OBJECTIVE: This study aimed to describe and evaluate the historical NRSS rabies-free county definition, review possibilities for improving this definition, and develop a model to achieve more precise estimates of the probability of terrestrial rabies freedom and the number of reported county-level terrestrial rabies cases. METHODS: Data submitted to the NRSS by state and territorial public health departments and the US Department of Agriculture Wildlife Services were analyzed to evaluate the historical rabies-free definition. A zero-inflated negative binomial model created county-level predictions of the probability of rabies freedom and the expected number of rabies cases reported. Data analyzed were from all animals submitted for laboratory diagnosis of rabies in the United States from 1995 to 2020 in skunk and raccoon reservoir territories, excluding bats and bat variants. RESULTS: We analyzed data from 14,642 and 30,120 county-years in the raccoon and skunk reservoir territories, respectively. Only 0.85% (9/1065) raccoon county-years and 0.79% (27/3411) skunk county-years that met the historical rabies-free criteria reported a case in the following year (99.2% negative predictive value for each), of which 2 were attributed to unreported bat variants. County-level model predictions displayed excellent discrimination for detecting zero cases and good estimates of reported cases in the following year. Counties classified as rabies free rarely (36/4476, 0.8%) detected cases in the following year. CONCLUSIONS: This study concludes that the historical rabies freedom definition is a reasonable approach for identifying counties that are truly free from terrestrial raccoon and skunk rabies virus transmission. Gradations of risk can be measured using the rabies prediction model presented in this study. However, even counties with a high probability of rabies freedom should maintain rabies testing capacity, as there are numerous examples of translocations of rabies-infected animals that can cause major changes in the epidemiology of rabies.

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19.

This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.