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Leptospirosis, an acute bacterial zoonotic disease, is endemic in Puerto Rico. Infection in approximately 10%-15% of patients with clinical disease progresses to severe, potentially fatal illness. Increased incidence has been associated with flooding in endemic areas around the world. In 2022, Hurricane Fiona, a Category 1 hurricane, made landfall and inundated Puerto Rico with heavy rainfall and severe flooding, increasing the risk for a leptospirosis outbreak. In response, the Puerto Rico Department of Health (PRDH) changed guidelines to make leptospirosis cases reportable within 24 hours, centralized the case investigation management system, and provided training and messaging to health care providers. To evaluate changes in risk for leptospirosis after Hurricane Fiona to that before the storm, the increase in cases was quantified, and patient characteristics and geographic distribution were compared. During the 15 weeks after Hurricane Fiona, 156 patients experienced signs and symptoms of leptospirosis and had a specimen with a positive laboratory result reported to PRDH. The mean weekly number of cases during this period was 10.4, which is 3.6 as high as the weekly number of cases during the previous 37 weeks (2.9). After Hurricane Fiona, the proportion of cases indicating exposure to potentially contaminated water increased from 11% to 35%, and the number of persons receiving testing increased; these factors likely led to the resulting overall surge in reported cases. Robust surveillance combined with outreach to health care providers after flooding events can improve leptospirosis case identification, inform clinicians considering early initiation of treatment, and guide public messaging to avoid wading, swimming, or any contact with potentially contaminated floodwaters., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Melissa Marzán-Rodríguez reported being a member of the board of directors of COAI, Inc. (LGBT Health). No other potential conflicts of interest were disclosed.

Next-generation sequencing technologies, exponential increases in the availability of virus genomic data, and ongoing advances in phylogenomic methods have made genomic epidemiology an increasingly powerful tool for public health response to a range of mosquito-borne virus outbreaks. In this review, we offer a brief primer on the scope and methods of phylogenomic analyses that can answer key epidemiological questions during mosquito-borne virus public health emergencies. We then focus on case examples of outbreaks, including those caused by dengue, Zika, yellow fever, West Nile, and chikungunya viruses, to demonstrate the utility of genomic epidemiology to support the prevention and control of mosquito-borne virus threats. We extend these case studies with operational perspectives on how to best incorporate genomic epidemiology into structured surveillance and response programs for mosquito-borne virus control. Many tools for genomic epidemiology already exist, but so do technical and nontechnical challenges to advancing their use. Frameworks to support the rapid sharing of multidimensional data and increased cross-sector partnerships, networks, and collaborations can support advancement on all scales, from research and development to implementation by public health agencies., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

In an Essay, Michael Johansson and colleagues advocate the posting of research studies addressing infectious disease outbreaks as preprints.

Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock., Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region., Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected., Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy., Funding: Wellcome Trust., (Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.)

To assist with public health preparedness activities, we estimated the number of expected cases of Zika virus in Puerto Rico and associated healthcare needs. Estimated annual incidence is 3.2-5.1 times the baseline, and long-term care needs are predicted to be 3-5 times greater than in years with no Zika virus.

Importance: Zika virus (ZIKV) infection during pregnancy is a cause of congenital microcephaly and severe fetal brain defects, and it has been associated with other adverse pregnancy and birth outcomes., Objective: To estimate the number of pregnant women infected with ZIKV in Puerto Rico and the number of associated congenital microcephaly cases., Design, Setting, and Participants: We conducted a modeling study from April to July 2016. Using parameters derived from published reports, outcomes were modeled probabilistically using Monte Carlo simulation. We used uncertainty distributions to reflect the limited information available for parameter values. Given the high level of uncertainty in model parameters, interquartile ranges (IQRs) are presented as primary results. Outcomes were modeled for pregnant women in Puerto Rico, which currently has more confirmed ZIKV cases than any other US location., Exposure: Zika virus infection in pregnant women., Main Outcomes and Measures: Number of pregnant women infected with ZIKV and number of congenital microcephaly cases., Results: We estimated an IQR of 5900 to 10 300 pregnant women (median, 7800) might be infected during the initial ZIKV outbreak in Puerto Rico. Of these, an IQR of 100 to 270 infants (median, 180) may be born with microcephaly due to congenital ZIKV infection from mid-2016 to mid-2017. In the absence of a ZIKV outbreak, an IQR of 9 to 16 cases (median, 12) of congenital microcephaly are expected in Puerto Rico per year., Conclusions and Relevance: The estimate of 5900 to 10 300 pregnant women that might be infected with ZIKV provides an estimate for the number of infants that could potentially have ZIKV-associated adverse outcomes. Including baseline cases of microcephaly, we estimated that an IQR of 110 to 290 total cases of congenital microcephaly, mostly attributable to ZIKV infection, could occur from mid-2016 to mid-2017 in the absence of effective interventions. The primary limitation in this analysis is uncertainty in model parameters. Multivariate sensitivity analyses indicated that the cumulative incidence of ZIKV infection and risk of microcephaly given maternal infection in the first trimester were the primary drivers of both magnitude and uncertainty in the estimated number of microcephaly cases. Increased information on these parameters would lead to more precise estimates. Nonetheless, the results underscore the need for urgent actions being undertaken in Puerto Rico to prevent congenital ZIKV infection and prepare for affected infants.

Jean-Paul Chretien and colleagues argue that recent Ebola and Zika virus outbreaks highlight the importance of data sharing in scientific research.

The recent emergence of dengue viruses into new susceptible human populations throughout Asia and the Middle East, driven in part by human travel on both local and global scales, represents a significant global health risk, particularly in areas with changing climatic suitability for the mosquito vector. In Pakistan, dengue has been endemic for decades in the southern port city of Karachi, but large epidemics in the northeast have emerged only since 2011. Pakistan is therefore representative of many countries on the verge of countrywide endemic dengue transmission, where prevention, surveillance, and preparedness are key priorities in previously dengue-free regions. We analyze spatially explicit dengue case data from a large outbreak in Pakistan in 2013 and compare the dynamics of the epidemic to an epidemiological model of dengue virus transmission based on climate and mobility data from ∼40 million mobile phone subscribers. We find that mobile phone-based mobility estimates predict the geographic spread and timing of epidemics in both recently epidemic and emerging locations. We combine transmission suitability maps with estimates of seasonal dengue virus importation to generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness.

Background: Like many infectious agents, yellow fever (YF) virus only causes disease in a proportion of individuals it infects and severe illness only represents the tip of the iceberg relative to the total number of infections, the more critical factor for virus transmission., Methods: We compiled data on asymptomatic infections, mild disease, severe disease (fever with jaundice or hemorrhagic symptoms) and fatalities from 11 studies in Africa and South America between 1969 and 2011. We used a Bayesian model to estimate the probability of each infection outcome., Results: For YF virus infections, the probability of being asymptomatic was 0.55 (95% credible interval [CI] 0.37-0.74), mild disease 0.33 (95% CI 0.13-0.52) and severe disease 0.12 (95% CI 0.05-0.26). The probability of death for people experiencing severe disease was 0.47 (95% CI 0.31-0.62)., Conclusions: In outbreak situations where only severe cases may initially be detected, we estimated that there may be between one and seventy infections that are either asymptomatic or cause mild disease for every severe case identified. As it is generally only the most severe cases that are recognized and reported, these estimates will help improve the understanding of the burden of disease and the estimation of the potential risk of spread during YF outbreaks., (Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Yellow fever virus is a global health threat due to its endemicity in parts of Africa and South America where human infections occur in residents and travelers. To understand yellow fever dynamics, it is critical to characterize the incubation periods of the virus in vector mosquitoes and humans. Here, we compare four statistical models of the yellow fever incubation periods fitted with historical data. The extrinsic incubation period in the urban vector Aedes aegypti was best characterized with a temperature-dependent Weibull model with a median of 10 days at 25 degrees C (middle 95% = 2.0-37 days). The intrinsic incubation period, fitted with a log-normal model, had a median of 4.3 days (middle 95% = 2.3-8.6 days). These estimates and their associated statistical models provide a quantitative basis to assist in exposure assessments, model potential outbreaks, and evaluate the effectiveness of public health interventions.