Background Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. Objective This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. Methods The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. Results Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.001). Conclusions This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics.

Zoonotic diseases, caused by pathogens transmitted between human and wildlife populations, most often emerge at human-wildlife interfaces where there is an increased likelihood of direct or indirect contact between people and infectious hosts and/or vectors (Hassell et al., 2017;Soulsbury & White, 2015). Urban landscapes are increasingly recognized as habitat for wildlife across both green

Background Rat-bite fever is a rare disease associated with rat bites or direct/indirect rodent contact. Methods We examined rat-bite fever and rat-bite injury diagnoses in the United States during 2001–2015. We analyzed national, state, and Indian Health Service healthcare encounter datasets for rat-bite fever and rat-bite injury diagnoses. We calculated average-annual encounter rates per 1 000 000 persons. Results Nationally, the rat-bite fever Emergency Department visit rate was 0.33 (95% confidence interval [CI], 0.19–0.47) and the hospitalization rate was 0.20 (95% CI, 0.17–0.24). The rat-bite injury Emergency Department visit rate was 10.51 (95% CI, 10.13–10.88) and the hospitalization rate was 0.27 (95% CI, 0.23–0.30). The Indian Health Service Emergency Department/outpatient visit rate was 3.00 for rat-bite fever and 18.89 for rat-bite injury. The majority of rat-bite fever encounters were among individuals 0–19 years of age. Conclusions Our results support the literature that rat-bite fever is rare and affects children and young adults. Targeted education could benefit specific risk groups.

Aedes albopictus is a vector of arboviruses with high rates of morbidity and mortality. The northern limit of Ae. albopictus in the northeastern United States runs through New York state (NYS) and Connecticut. We present a landscape-level analysis of mosquito abundance measured by daily counts of Ae. albopictus from 338 trap sites in 12 counties during May-September 2017. During the study period, the mean number of Ae. albopictus caught per day of trapping across all sites was 3.21. We constructed four sets of negative binomial generalized linear models to evaluate how trapping methodology, land cover, as well as temperature and precipitation at multiple time intervals influenced Ae. albopictus abundance. Biogents-Sentinel (BGS) traps were 2.78 times as efficient as gravid traps and 1.49 times as efficient as CO 2 -baited CDC light traps. Greater proportions of low-and medium-intensity development and low proportions of deciduous cover around the trap site were positively associated with increased abundance, as were minimum winter temperature and March precipitation. The cumulative precipitation within a 28-day time window before the date of collection had a nonlinear relationship with abundance, such that greater cumulative precipitation was associated with increased abundance until approximately 70 mm, above which there was a decrease in abundance. We concluded that populations are established in Nassau, Suffolk, and New York City counties in NYS; north of these counties, the species is undergoing population invasion and establishment. We recommend that mosquito surveillance programs monitoring the northward invasion of Ae. albopictus place BGS traps at sites chosen with respect to land cover.

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. in the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases' environmental suitability (AeDeS) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies. Human society is more and more interconnected every year by communication technologies, travel and supply chains. As a consequence, increasing movement of humans, animals, pathogens, vectors, goods, and capital across borders creates both risks and opportunities 1. Like climate, epidemics do not mind political borders, and can impact social stability and human health. In the last couple of decades, the appearance of a variety of new epidemics, such as the SARS coronavirus in 2003, the avian influenza (H1N1) in 2009, the Ebola virus in western Africa (2014-2016), the Zika virus in the Americas (2015-2016), and the novel coronavirus (SARS-CoV-2) identified in late December 2019 in Wuhan (China) and still ongoing, amongst others, demonstrates how fast emerging infectious diseases can spread, sometimes causing damage at national or regional scale, and other times-like the present SARS-CoV-2 pandemic-impacting the entire world 2. Multiple infectious diseases are climate-sensitive, with climate acting as a key driver of spatio-temporal patterns of infections, related to seasonal, year-to-year, and longer-term shifts in populations at risk 3. Climate

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Pneumonia is a leading cause of death in New York City (NYC). We identified spatial clusters of pneumonia-associated hospitalisation for persons residing in NYC, aged ⩾18 years during 2010–2014. We detected pneumonia-associated hospitalisations using an all-payer inpatient dataset. Using geostatistical semivariogram modelling, local Moran'sIcluster analyses andχ2tests, we characterised differences between ‘hot spots’ and ‘cold spots’ for pneumonia-associated hospitalisations. During 2010–2014, there were 141 730 pneumonia-associated hospitalisations across 188 NYC neighbourhoods, of which 43.5% (N= 61 712) were sub-classified as severe. Hot spots of pneumonia-associated hospitalisation spanned 26 neighbourhoods in the Bronx, Manhattan and Staten Island, whereas cold spots were found in lower Manhattan and northeastern Queens. We identified hot spots of severe pneumonia-associated hospitalisation in the northern Bronx and the northern tip of Staten Island. For severe pneumonia-associated hospitalisations, hot-spot patients were of lower mean age and a greater proportion identified as non-Hispanic Black compared with cold spot patients; additionally, hot-spot patients had a longer hospital stay and a greater proportion experienced in-hospital death compared with cold-spot patients. Pneumonia prevention efforts within NYC should consider examining the reasons for higher rates in hot-spot neighbourhoods, and focus interventions towards the Bronx, northern Manhattan and Staten Island.