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Since the COVID-19 pandemic, attention and investment in pandemic preparedness have increased. Although there are many valiant plans around pandemic preparedness, they typically involve slowing the spread or mitigating the effects of a pathogen after it has already entered the human population. The task of stopping the pathogen from entering the human population in the first place, spillover prevention, remains a neglected area in discussions and planning for pandemic risk mitigation. Every spillover offers an opportunity to learn about an emerging public health threat and the conditions that aligned to enable spillover occurrence. In this article, we outline One Health approaches for use in spillover investigations, drawing from our experience investigating Hendra and Nipah virus spillovers. We present a roadmap for how findings from those investigations can lead to the development of interventions for spillover and ultimately pandemic prevention.

In the past two decades, three coronaviruses with ancestral origins in bats have emerged and caused widespread outbreaks in humans, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first SARS epidemic in 2002–2003, the appreciation of bats as key hosts of zoonotic coronaviruses has advanced rapidly. More than 4,000 coronavirus sequences from 14 bat families have been identified, yet the true diversity of bat coronaviruses is probably much greater. Given that bats are the likely evolutionary source for several human coronaviruses, including strains that cause mild upper respiratory tract disease, their role in historic and future pandemics requires ongoing investigation. We review and integrate information on bat–coronavirus interactions at the molecular, tissue, host and population levels. We identify critical gaps in knowledge of bat coronaviruses, which relate to spillover and pandemic risk, including the pathways to zoonotic spillover, the infection dynamics within bat reservoir hosts, the role of prior adaptation in intermediate hosts for zoonotic transmission and the viral genotypes or traits that predict zoonotic capacity and pandemic potential. Filling these knowledge gaps may help prevent the next pandemic.Bats harbour a multitude of coronaviruses and owing to their diversity and wide distribution are prime reservoir hosts of emerging viruses. Ruiz-Aravena, McKee and colleagues analyse the currently available information on bat coronaviruses and discuss their role in recent and potential future spillovers.

Whether an individual becomes infected in an infectious disease outbreak depends on many interconnected risk factors, which may relate to characteristics of the individual (e.g., age, sex), his or her close relatives (e.g., household members), or the wider community. Studies monitoring individuals in households or schools have helped elucidate the determinants of transmission in small social structures due to advances in statistical modeling; but such an approach has so far largely failed to consider individuals in the wider context they live in. Here, we used an outbreak of chikungunya in a rural community in Bangladesh as a case study to obtain a more comprehensive characterization of risk factors in disease spread. We developed Bayesian data augmentation approaches to account for uncertainty in the source of infection, recall uncertainty, and unobserved infection dates. We found that the probability of chikungunya transmission was 12% [95% credible interval (CI): 8–17%] between household members but dropped to 0.3% for those living 50 m away (95% CI: 0.2–0.5%). Overall, the mean transmission distance was 95 m (95% CI: 77–113 m). Females were 1.5 times more likely to become infected than males (95% CI: 1.2–1.8), which was virtually identical to the relative risk of being at home estimated from an independent human movement study in the country. Reported daily use of antimosquito coils had no detectable impact on transmission. This study shows how the complex interplay between the characteristics of an individual and his or her close and wider environment contributes to the shaping of infectious disease epidemics.

Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. Effective test-trace-isolate programs first need to be strong in the \"test\" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

Nipah virus is a highly virulent zoonotic pathogen. In this report from Bangladesh, which included 40% of the world’s known cases, the risk factors for human-to-human transmission were evaluated. No asymptomatic cases were identified. Increased respiratory symptoms in the patient and prolonged close contact from caregivers were associated with secondary transmission.

The 2018 outbreak of Nipah virus in Kerala, India, highlights the need for global surveillance of henipaviruses in bats, which are the reservoir hosts for this and other viruses. Nipah virus, an emerging paramyxovirus in the genus Henipavirus, causes severe disease and stuttering chains of transmission in humans and is considered a potential pandemic threat. In May 2018, an outbreak of Nipah virus began in Kerala, > 1800 km from the sites of previous outbreaks in eastern India in 2001 and 2007. Twenty-three people were infected and 21 people died (16 deaths and 18 cases were laboratory confirmed). Initial surveillance focused on insectivorous bats (Megaderma spasma), whereas follow-up surveys within Kerala found evidence of Nipah virus in fruit bats (Pteropus medius). P. medius is the confirmed host in Bangladesh and is now a confirmed host in India. However, other bat species may also serve as reservoir hosts of henipaviruses. To inform surveillance of Nipah virus in bats, we reviewed and analyzed the published records of Nipah virus surveillance globally. We applied a trait-based machine learning approach to a subset of species that occur in Asia, Australia, and Oceana. In addition to seven species in Kerala that were previously identified as Nipah virus seropositive, we identified at least four bat species that, on the basis of trait similarity with known Nipah virus-seropositive species, have a relatively high likelihood of exposure to Nipah or Nipah-like viruses in India. These machine-learning approaches provide the first step in the sequence of studies required to assess the risk of Nipah virus spillover in India. Nipah virus surveillance not only within Kerala but also elsewhere in India would benefit from a research pipeline that included surveys of known and predicted reservoirs for serological evidence of past infection with Nipah virus (or cross reacting henipaviruses). Serosurveys should then be followed by longitudinal spatial and temporal studies to detect shedding and isolate virus from species with evidence of infection. Ecological studies will then be required to understand the dynamics governing prevalence and shedding in bats and the contacts that could pose a risk to public health.

Nipah virus spillovers via consumption of date palm sap in Bangladesh vary substantially between years and have been associated with lower winter temperatures and precipitation. However, the mechanisms driving the interannual variation and the influence of weather remain unexplained. Here we investigated the hypothesis that weather patterns change human sap consumption and explain interannual variation in observed spillovers. We analyzed responses from a nationally representative survey conducted in Bangladesh in 2013–2016 on household date palm sap consumption and weather data for each division of Bangladesh, using logistic regression to examine whether sap consumption is associated with weather variability. We found significant associations of lower minimum temperatures and precipitation with increased household sap consumption during the sap harvesting season. This relationship was largely similar within all months and divisions, and strong associations of temperature (χ 2 (1, n = 5,027) = 7.74, p < 0.01) and, independently, precipitation (χ 2 = 8.00, p < 0.01) remained strong after accounting for month, location, and annual sap season. Interannual variation in date palm sap consumption in Bangladesh is likely best explained by temperature and precipitation patterns, where colder, drier winter days pose a higher risk for Nipah virus spillover. The knowledge gained in this study may be valuable for targeting timing of future behavioral interventions against consumption of date palm sap in Bangladesh.

Nipah virus (NiV) is a paramyxovirus whose reservoir host is fruit bats of the genus Pteropus . Occasionally the virus is introduced into human populations and causes severe illness characterized by encephalitis or respiratory disease. The first outbreak of NiV was recognized in Malaysia, but 8 outbreaks have been reported from Bangladesh since 2001. The primary pathways of transmission from bats to people in Bangladesh are through contamination of raw date palm sap by bats with subsequent consumption by humans and through infection of domestic animals (cattle, pigs, and goats), presumably from consumption of food contaminated with bat saliva or urine with subsequent transmission to people. Approximately one-half of recognized Nipah case patients in Bangladesh developed their disease following person-to-person transmission of the virus. Efforts to prevent transmission should focus on decreasing bat access to date palm sap and reducing family members' and friends' exposure to infected patients' saliva.

Neonatal infections are increasingly caused by antibiotic-resistant bacteria. It is unknown to what extent home-based births, which account for nearly a third of deliveries in Bangladesh, may also result in exposure to antibiotic-resistant bacteria. We enrolled mothers who delivered at home and their newborns from a rural community in Bangladesh during April-June, 2022. Within 2-7 days after delivery, we collected vaginal and rectal swabs from mothers and rectal swabs from the newborns. Swabs were cultured on chromogenic culture media selective for extended-spectrum β-lactamase-producing bacteria (ESBL-PB) and carbapenem-resistant bacteria (CRB). Demographic and risk factor data were collected via surveys. Birth attendants who facilitated the deliveries were interviewed regarding infection prevention practices. We performed descriptive analyses and Firth's penalized logistic regression to identify potential risk factors associated with colonization. Of the 50 mothers enrolled, the median age was 23 years (range 18-26). Thirty-eight (76%) mothers had at least one antenatal care visit. Only one mother reported hospitalization during pregnancy, and 4 reported antibiotic use during pregnancy. Following delivery, 47 (94%) mothers were colonized with ESBL-PB, and 37 (74%) were colonized with CRB. Of the newborns, 36 (72%) were colonized with ESBL-PB, and 27 (54%) were colonized with CRB. No associations were found with any perinatal exposures, though all households reported incomes below the international poverty level. Of the 9 birth attendants who were able to be interviewed, 7 (78%) reported performing hand hygiene before delivery, and 8 (89%) reported glove use during delivery. Attendants reported cleaning equipment shared across deliveries with soap and water and using boiled water for delivery (89%, n = 8). Women and newborns in this rural population were frequently colonized with both ESBL-PB and CRB following home deliveries. This demonstrates the importance of community-based antibiotic-resistant bacterial transmission and need for further understanding community exposures driving antibiotic resistance.

Maternal hypertension may be an underrecognized but important risk factor for perinatal death in low resource settings. We investigated the association of maternal hypertension and perinatal mortality in rural Bangladesh. This nested, matched case-control study used data from a 2019 cross-sectional survey and demographic surveillance database in Baliakandi, Bangladesh. We randomly matched each pregnancy ending in perinatal death with five pregnancies in which the neonate survived beyond seven days based on maternal age, education, and wealth quintile. We estimated associations of antenatal care-seeking and self-reported hypertension with perinatal mortality using conditional logistic regression and used median and interquartile ranges to assess the mediation of antenatal care by timing or frequency. Among 191 cases and 934 matched controls, hypertension prevalence was 14.1% among cases and 7.7% among controls. Compared with no diagnosis, the probability of perinatal death was significantly higher among women with a pre-gestational hypertension diagnosis (OR 2.90, 95% CI 1.29, 6.57), but not among women with diagnosis during pregnancy (OR 1.68, 95% CI 0.98, 2.98). We found no association between the number of antenatal care contacts and perinatal death (p = 0.66). Among women with pre-gestational hypertension who experienced a perinatal death, 78% had their first antenatal contact in the sixth or seventh month of gestation. Hypertension was more common among rural women who experience a perinatal death. Greater effort to prevent hypertension prior to conception and provide early maternity care to women with hypertension could improve perinatal outcomes in rural Bangladesh.