The impact of Zika emergence in remote communities in northwestern Ecuador.

The Zika virus (ZIKV) epidemic in Latin America (2015-2016) has primarily been studied in urban centers, with less understanding of its impact on smaller rural communities. To address this gap, we analyzed ZIKV sero-epidemiology in six rural Ecuadorian communities (2018-2019) with varying access to a commercial hub. Seroprevalence ranged from 19% to 54% measured by NS1 blockade of binding ELISA. We observed a decline in ZIKV seroprevalence between 2018 and 2019 that was greater among younger populations, suggesting that the attack rates in the 2015-16 epidemic were significantly higher than our 2018 observations. These data indicate that the 2015-16 epidemic included significant transmission in rural and more remote settings. Our observations of high seroprevalence in our area of study highlights the importance of surveillance and research in rural areas lacking robust health systems to manage future Zika outbreaks and vaccine initiatives.

Shared sanitation facilities and risk of respiratory virus transmission in resource-poor settings: A COVID-19 modeling case study.

Water supply and sanitation are essential household services frequently shared in resource-poor settings. Shared sanitation can increase the risk of enteric pathogen transmission due to suboptimal cleanliness of facilities used by large numbers of individuals. It also can potentially increase the risk of respiratory disease transmission. As sanitation is an essential need, shared sanitation facilities may act as important respiratory pathogen transmission venues even with strict control measures such as stay-at-home recommendations in place. This analysis explores how behavioral and infrastructural conditions surrounding shared sanitation may individually and interactively influence respiratory pathogen transmission. We developed an individual-based community transmission model using COVID-19 as a motivating example parameterized from empirical literature to explore how transmission in shared latrines interacts with transmission at the community level. We explored mitigation strategies, including infrastructural and behavioral interventions. Our review of empirical literature confirms that shared sanitation venues in resource-poor settings are relatively small with poor ventilation and high use patterns. In these contexts, shared sanitation facilities may act as strong drivers of respiratory disease transmission, especially in areas reliant on shared facilities. Decreasing dependence on shared latrines was most effective at attenuating sanitation-associated transmission. Improvements to latrine ventilation and handwashing behavior were also able to decrease transmission. The type and order of interventions are important in successfully attenuating disease risk, with infrastructural and engineering controls being most effective when administered first, followed by behavioral controls after successful attenuation of sufficient alternate transmission routes. Beyond COVID-19, our modeling framework can be extended to address water, sanitation, and hygiene measures targeted at a range of environmentally mediated infectious diseases.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Phylogenetic Analysis of Transmission Dynamics of Dengue in Large and Small Population Centers, Northern Ecuador.

Although dengue is typically considered an urban disease, rural communities are also at high risk. To clarify dynamics of dengue virus (DENV) transmission in settings with characteristics generally considered rural (e.g., lower population density, remoteness), we conducted a phylogenetic analysis in 6 communities in northwestern Ecuador. DENV RNA was detected by PCR in 121/488 serum samples collected from febrile case-patients during 2019-2021. Phylogenetic analysis of 27 samples from Ecuador and other countries in South America confirmed that DENV-1 circulated during May 2019-March 2020 and DENV-2 circulated during December 2020-July 2021. Combining locality and isolation dates, we found strong evidence that DENV entered Ecuador through the northern province of Esmeraldas. Phylogenetic patterns suggest that, within this province, communities with larger populations and commercial centers were more often the source of DENV but that smaller, remote communities also play a role in regional transmission dynamics.

Trade-off Between Quarantine Length and Compliance to Optimize COVID-19 Control.

BACKGROUND: Guidance on COVID-19 quarantine duration is often based on the maximum observed incubation periods assuming perfect compliance. However, the impact of longer quarantines may be subject to diminishing returns; the largest benefits of quarantine occur over the first few days. Additionally, the financial and psychological burdens of quarantine may motivate increases in noncompliance behavior. METHODS: We use a deterministic transmission model to identify the optimal length of quarantine to minimize transmission. We modeled the relation between noncompliance behavior and disease risk using a time-varying function of leaving quarantine based on studies from the literature. RESULTS: The first few days in quarantine were more crucial to control the spread of COVID-19; even when compliance is high, a 10-day quarantine was as effective in lowering transmission as a 14-day quarantine; under certain noncompliance scenarios a 5-day quarantine may become nearly protective as 14-day quarantine. CONCLUSION: Data to characterize compliance dynamics will help select optimal quarantine strategies that balance the trade-offs between social forces governing behavior and transmission dynamics.

Leveraging infectious disease models to interpret randomized controlled trials: Controlling enteric pathogen transmission through water, sanitation, and hygiene interventions.

Randomized controlled trials (RCTs) evaluate hypotheses in specific contexts and are often considered the gold standard of evidence for infectious disease interventions, but their results cannot immediately generalize to other contexts (e.g., different populations, interventions, or disease burdens). Mechanistic models are one approach to generalizing findings between contexts, but infectious disease transmission models (IDTMs) are not immediately suited for analyzing RCTs, since they often rely on time-series surveillance data. We developed an IDTM framework to explain relative risk outcomes of an infectious disease RCT and applied it to a water, sanitation, and hygiene (WASH) RCT. This model can generalize the RCT results to other contexts and conditions. We developed this compartmental IDTM framework to account for key WASH RCT factors: i) transmission across multiple environmental pathways, ii) multiple interventions applied individually and in combination, iii) adherence to interventions or preexisting conditions, and iv) the impact of individuals not enrolled in the study. We employed a hybrid sampling and estimation framework to obtain posterior estimates of mechanistic parameter sets consistent with empirical outcomes. We illustrated our model using WASH Benefits Bangladesh RCT data (n = 17,187). Our model reproduced reported diarrheal prevalence in this RCT. The baseline estimate of the basic reproduction number [Formula: see text] for the control arm (1.10, 95% CrI: 1.07, 1.16) corresponded to an endemic prevalence of 9.5% (95% CrI: 7.4, 13.7%) in the absence of interventions or preexisting WASH conditions. No single pathway was likely able to sustain transmission: pathway-specific [Formula: see text] for water, fomites, and all other pathways were 0.42 (95% CrI: 0.03, 0.97), 0.20 (95% CrI: 0.02, 0.59), and 0.48 (95% CrI: 0.02, 0.94), respectively. An IDTM approach to evaluating RCTs can complement RCT analysis by providing a rigorous framework for generating data-driven hypotheses that explain trial findings, particularly unexpected null results, opening up existing data to deeper epidemiological understanding.

Multiscalar Evaluation of the Water Distribution System and Diarrheal Disease Risk in Addis Ababa, Ethiopia.

Despite growing urbanization, our understanding of the impacts of water and sanitation on human health has largely come from studies in rural sectors. To this end, we collected data at both regional (water quality measures from water treatment systems) and community (cross-sectional surveys) scales to examine determinants of enteric pathogen infection and diarrheal disease among infants in Addis Ababa, Ethiopia. Regionally, the Legedadi water treatment plant had significantly lower heterotrophic plate counts, total coliform counts, and fecal coliform counts compared with the Gefersa water treatment plant. The number of pathogen types in infant stool also differed by plant. Decreases in chlorine levels and increases in the relative abundance of Gammaproteobacteria with distance from treatment plants suggest a compromised water distribution system. In communities, infants in households that obtained water from yard pipes or public taps had significantly lower odds of diarrhea compared to households that had water piped into their dwellings (OR = 0.35, 95% CI 0.16, 0.76, and OR = 0.39, 95% CI 0.15, 1.00, respectively). Similarly, infants in households that boiled or filtered water had significantly lower odds of diarrhea compared to households that did not treat water (OR = 0.40, 95% CI 0.19, 0.86 and OR = 0.23, 95% CI 0.06, 0.84, respectively). Integrating multiscalar data better informs the health impacts of water in urban settings.

Dilution of Epidemic Potential of Environmentally Transmitted Infectious Diseases for Species with Partially Overlapping Habitats.

AbstractSpecies diversity may play an important role in the modulation of pathogen transmission through the dilution effect. Infectious disease models can help elucidate mechanisms that may underlie this effect. While many modeling studies have assumed direct host-to-host transmission, many pathogens are transmitted through the environment. We present a mathematical modeling analysis exploring conditions under which we observe the dilution effect in systems with environmental transmission where host species interact through fully or partially overlapping habitats. We measure the strength of the dilution effect by the relative decrease in the basic reproduction number of two-species assemblages compared with that of a focal host species. We find that a dilution effect is most likely when the pathogen is environmentally persistent (frequency-dependent-like transmission). The magnitude of this effect is strongest when the species with the greater epidemic potential is relatively slow to pick up pathogens in the environment (density-dependent transmission) and the species with the lesser epidemic potential is efficient at picking up pathogens (frequency-dependent transmission). These findings suggest that measurable factors, including pathogen persistence and the host's relative efficiency of pathogen pickup, can guide predictions of when biodiversity might lead to a dilution effect and may thus give concrete direction to future ecological work.

Immunologic and Epidemiologic Drivers of Norovirus Transmission in Daycare and School Outbreaks.

BACKGROUND: Norovirus outbreaks are notoriously explosive, with dramatic symptomology and rapid disease spread. Children are particularly vulnerable to infection and drive norovirus transmission due to their high contact rates with each other and the environment. Despite the explosive nature of norovirus outbreaks, attack rates in schools and daycares remain low with the majority of students not reporting symptoms. METHODS: We explore immunologic and epidemiologic mechanisms that may underlie epidemic norovirus transmission dynamics using a disease transmission model. Towards this end, we compared different model scenarios, including innate resistance and acquired immunity (collectively denoted 'immunity'), stochastic extinction, and an individual exclusion intervention. We calibrated our model to daycare and school outbreaks from national surveillance data. RESULTS: Including immunity in the model led to attack rates that were consistent with the data. However, immunity alone resulted in the majority of outbreak durations being relatively short. The addition of individual exclusion (to the immunity model) extended outbreak durations by reducing the amount of time that symptomatic people contribute to transmission. Including both immunity and individual exclusion mechanisms resulted in simulations where both attack rates and outbreak durations were consistent with surveillance data. CONCLUSIONS: The epidemiology of norovirus outbreaks in daycare and school settings cannot be well described by a simple transmission model in which all individuals start as fully susceptible. More studies on how best to design interventions which leverage population immunity and encourage more rigorous individual exclusion may improve venue-level control measures. See video abstract at http://links.lww.com/EDE/B795.

An urban-to-rural continuum of malaria risk: new analytic approaches characterize patterns in Malawi.

BACKGROUND: The urban-rural designation has been an important risk factor in infectious disease epidemiology. Many studies rely on a politically determined dichotomization of rural versus urban spaces, which fails to capture the complex mosaic of infrastructural, social and environmental factors driving risk. Such evaluation is especially important for Plasmodium transmission and malaria disease. To improve targeting of anti-malarial interventions, a continuous composite measure of urbanicity using spatially-referenced data was developed to evaluate household-level malaria risk from a house-to-house survey of children in Malawi. METHODS: Children from 7564 households from eight districts throughout Malawi were tested for presence of Plasmodium parasites through finger-prick blood sampling and slide microscopy. A survey questionnaire was administered and latitude and longitude coordinates were recorded for each household. Distances from households to features associated with high and low levels of development (health facilities, roads, rivers, lakes) and population density were used to produce a principal component analysis (PCA)-based composite measure for all centroid locations of a fine geo-spatial grid covering Malawi. Regression methods were used to test associations of the urbanicity measure against Plasmodium infection status and to predict parasitaemia risk for all locations in Malawi. RESULTS: Infection probability declined with increasing urbanicity. The new urbanicity metric was more predictive than either a governmentally defined rural/urban dichotomous variable or a population density variable. One reason for this was that 23% of cells within politically defined rural areas exhibited lower risk, more like those normally associated with "urban" locations. CONCLUSIONS: In addition to increasing predictive power, the new continuous urbanicity metric provided a clearer mechanistic understanding than the dichotomous urban/rural designations. Such designations often ignore urban-like, low-risk pockets within traditionally rural areas, as were found in Malawi, along with rural-like, potentially high-risk environments within urban areas. This method of characterizing urbanicity can be applied to other infectious disease processes in rapidly urbanizing contexts.

Determinants of Pathogen Contamination of the Environment in the Greater Yangon Area, Myanmar.

Recent results from water, sanitation, and hygiene interventions highlight the need to better understand environmental influences on enteropathogen transmission. We quantified a range of viral, bacterial, and protozoal pathogens and one indicator, Enterococcus faecalis in soil and water from urban and rural sites in and around Yangon, Myanmar. We found that environmental characteristics associated with contamination differed by pathogens and substrates. In soil, bacterial pathogen gene counts were associated with elevation and drainage ditches (compared to stagnant water) (RR = 0.96, 95% CI 0.93, 0.99 and RR = 1.70, 95% CI 1.18, 2.45, respectively), while viral gene counts were associated with the presence of sanitation facilities within 50 m of the collection point (RR = 3.99, 95% CI 1.12, 14.24). In water, E. faecalis, total pathogen, and bacterial pathogen gene counts were associated with drainage ditches (RR = 1.86, 95% CI 1.27, 2.72, RR = 1.38 95% CI 1.09, 1.74, and RR = 1.38 95% CI 1.07, 1.77, respectively). E. faecalis, total pathogen, bacterial pathogen, and viral gene counts were associated with the presence of uncollected garbage within 50 m of the collection point (RR = 1.57, 95% CI 1.00, 2.47, RR = 1.52, 95% CI 1.16, 2.00, RR = 1.52, 95% CI 1.13, 2.06, and RR = 1.75, 95% CI 1.17, 2.61 respectively). Measuring the environment provides added specificity toward identifying important environmental pathways that require mitigation.

Modeling environmentally mediated rotavirus transmission: The role of temperature and hydrologic factors.

Rotavirus is considered a directly transmitted disease due to its high infectivity. Environmental pathways have, therefore, largely been ignored. Rotavirus, however, persists in water sources, and both its surface water concentrations and infection incidence vary with temperature. Here, we examine the potential for waterborne rotavirus transmission. We use a mechanistic model that incorporates both direct and waterborne transmission pathways, coupled with a hydrological model, and we simulate rotavirus transmission between two communities with interconnected water sources. To parameterize temperature dependency, we estimated temperature-dependent decay rates in water through a meta-analysis. Our meta-analysis suggests that rotavirus decay rates are positively associated with temperature (n = 39, P [Formula: see text] 0.001). This association is stronger at higher temperatures (over 20 °C), consistent with tropical climate conditions. Our model analysis demonstrates that water could disseminate rotavirus between the two communities for all modeled temperatures. While direct transmission was important for disease amplification within communities, waterborne transmission could also amplify transmission. In standing-water systems, the modeled increase in decay led to decreased disease, with every 1 °C increase in temperature leading to up to a 2.4% decrease in incidence. These effect sizes are consistent with prior meta-analyses, suggesting that environmental transmission through water sources may partially explain the observed associations between temperature and rotavirus incidence. Waterborne rotavirus transmission is likely most important in cooler seasons and in communities that use slow-moving or stagnant water sources. Even when indirect transmission through water cannot sustain outbreaks, it can seed outbreaks that are maintained by high direct transmission rates.

Epidemiology of the silent polio outbreak in Rahat, Israel, based on modeling of environmental surveillance data.

Israel experienced an outbreak of wild poliovirus type 1 (WPV1) in 2013-2014, detected through environmental surveillance of the sewage system. No cases of acute flaccid paralysis were reported, and the epidemic subsided after a bivalent oral polio vaccination (bOPV) campaign. As we approach global eradication, polio will increasingly be detected only through environmental surveillance. We developed a framework to convert quantitative polymerase chain reaction (qPCR) cycle threshold data into scaled WPV1 and OPV1 concentrations for inference within a deterministic, compartmental infectious disease transmission model. We used this approach to estimate the epidemic curve and transmission dynamics, as well as assess alternate vaccination scenarios. Our analysis estimates the outbreak peaked in late June, much earlier than previous estimates derived from analysis of stool samples, although the exact epidemic trajectory remains uncertain. We estimate the basic reproduction number was 1.62 (95% CI 1.04-2.02). Model estimates indicate that 59% (95% CI 9-77%) of susceptible individuals (primarily children under 10 years old) were infected with WPV1 over a little more than six months, mostly before the vaccination campaign onset, and that the vaccination campaign averted 10% (95% CI 1-24%) of WPV1 infections. As we approach global polio eradication, environmental monitoring with qPCR can be used as a highly sensitive method to enhance disease surveillance. Our analytic approach brings public health relevance to environmental data that, if systematically collected, can guide eradication efforts.

Associations between livestock ownership and lower odds of anaemia among children 6-59 months old are not mediated by animal-source food consumption in Ghana.

Livestock ownership may mitigate anaemia among young children by providing access to animal-source foods (ASFs) yet exacerbate anaemia by exposing children to animal-source pathogens. This study aimed to assess the association between household livestock ownership and child anaemia and examine whether this relationship is mediated by child ASF consumption or by child morbidity and inflammation. We conducted a cross-sectional study of 470 children aged 6-59 months in Greater Accra, Ghana. Child blood samples were analysed for haemoglobin concentration, iron status biomarkers and inflammatory biomarkers. Caregivers were asked about the child's frequency of ASF consumption in the past 3 months. Livestock ownership was categorized into five typologies to distinguish households by the number and combinations of species owned. In adjusted logistic regression, children from households in Type 5, owning cattle, small livestock (goats, sheep or pigs) and poultry, had lower odds of anaemia compared with those in Type 1, owning no livestock (OR [95% CI]: 0.32 [0.14, 0.71]). Although children from households that owned poultry were more likely to consume chicken meat, and children from households with cattle were more likely to drink cow's milk, consumption of these ASFs did not mediate the observed association between livestock ownership and child anaemia. There were no associations between livestock ownership and children's symptoms of illness or inflammation. Further research is needed to understand how ownership of certain livestock species, or a greater diversity of livestock species, may be associated with the risk of child anaemia, including the role of dietary and income-based pathways.

The Impact of Vaccination Efforts on the Spatiotemporal Patterns of the Hepatitis A Outbreak in Michigan, 2016-2018.

BACKGROUND: The United States is currently experiencing the largest hepatitis A virus (HAV) outbreak since the introduction of a vaccine in 1996. More than 31,000 cases have been reported since 2016. Although HAV had largely been considered a foodborne pathogen in recent years, this outbreak has been spread primarily through person-to-person transmission in urban settings and has been associated with homelessness and substance use. Michigan was one of the first states to report an outbreak, with 910 reported cases between August 2016 and December 2018. METHODS: We analyzed surveillance and vaccination data from Michigan using a disease transmission model to investigate how vaccine timing and coverage influenced the spatiotemporal patterns of the outbreak, distinguishing between Southeast Michigan, where the outbreak began, and the rest of the state. RESULTS: We estimated that vaccination had little impact in Southeast Michigan (3% cases averted [95% confidence interval (CI) = 1%, 8%]) but had a substantial impact in the rest of the state, preventing a larger outbreak (91% cases averted [95% CI = 85%, 97%]) lasting several more years. CONCLUSIONS: Our results emphasize the value of targeting populations where local transmission is not yet sustained rather than populations where transmission is already waning. Simulation modeling can aid in proactive rather than reactive decision-making and may help direct the response to outbreaks emerging in other states. See video abstract: http://links.lww.com/EDE/B704.

Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group.

Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.

Mass Gatherings and Diarrheal Disease Transmission Among Rural Communities in Coastal Ecuador.

Mass gatherings exacerbate infectious disease risks by creating crowded, high-contact conditions and straining the capacity of local infrastructure. While mass gatherings have been extensively studied in the context of epidemic disease transmission, the role of gatherings in incidence of high-burden, endemic infections has not been previously studied. Here, we examine diarrheal incidence among 17 communities in Esmeraldas, Ecuador, in relation to recurrent gatherings characterized using ethnographic data collected during and after the epidemiologic surveillance period (2004-2007). Using distributed-lag generalized estimating equations, adjusted for seasonality, trend, and heavy rainfall events, we found significant increases in diarrhea risk in host villages, peaking 2 weeks after an event's conclusion (incidence rate ratio, 1.21; confidence interval, adjusted for false coverage rate of ≤0.05: 1.02, 1.43). Stratified analysis revealed heightened risks associated with events where crowding and travel were most likely (2-week-lag incidence rate ratio, 1.51; confidence interval, adjusted for false coverage rate of ≤0.05: 1.09, 2.10). Our findings suggest that community-scale mass gatherings might play an important role in endemic diarrheal disease transmission and could be an important focus for interventions to improve community health in low-resource settings.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

Phenotypic variations in persistence and infectivity between and within environmentally transmitted pathogen populations impact population-level epidemic dynamics.

BACKGROUND: Human pathogens transmitted through environmental pathways are subject to stress and pressures outside of the host. These pressures may cause pathogen pathovars to diverge in their environmental persistence and their infectivity on an evolutionary time-scale. On a shorter time-scale, a single-genotype pathogen population may display wide variation in persistence times and exhibit biphasic decay. METHODS: We use a transmission modeling framework to develop an infectious disease model with biphasic pathogen decay. We take a differential algebra approach to assessing model identifiability, calculate basic reproduction numbers by the next generation method, and use simulation to explore model dynamics. RESULTS: For both long and short time-scales, we demonstrate that epidemic-potential-preserving trade-offs have implications for epidemic dynamics: less infectious, more persistent pathogens cause epidemics to progress more slowly than more infectious, less persistent (labile) pathogens, even when the overall risk is the same. Using identifiability analysis, we show that the usual disease surveillance data does not sufficiently inform these underlying pathogen population dynamics, even when combined with basic environmental monitoring data. However, risk could be indirectly ascertained by developing methods to separately monitor labile and persistent subpopulations. Alternatively, determining the relative infectivity of persistent pathogen subpopulations and the rates of phenotypic conversion will help ascertain how much disease risk is associated with the long tails of biphasic decay. CONCLUSION: A better understanding of persistence-infectivity trade-offs and associated dynamics can improve our ecological understanding of environmentally transmitted pathogens, as well as our risk assessment and disease control strategies.

Social cohesion and passive adaptation in relation to climate change and disease.

Climate change affects biophysical processes related to the transmission of many infectious diseases, with potentially adverse consequences for the health of communities. While our knowledge of biophysical associations between meteorological factors and disease is steadily improving, our understanding of the social processes that shape adaptation to environmental perturbations lags behind. Using computational modeling methods, we explore the ways in which social cohesion can affect adaptation of disease prevention strategies when communities are exposed to different environmental scenarios that influence transmission pathways for diseases such as diarrhea. We developed an agent-based model in which household agents can choose between two behavioral strategies that offer different levels of protection against environmentally mediated disease transmission. One behavioral strategy is initially set as more protective, leading households to adopt it widely, but its efficacy is sensitive to variable weather conditions and stressors such as floods or droughts that modify the disease transmission system. The efficacy of the second strategy is initially moderate relative to the first and is insensitive to environmental changes. We examined how social cohesion (defined as average number of household social network connections) influences health outcomes when households attempt to identify an optimal strategy by copying the behaviors of socially connected neighbors who seem to have adapted successfully in the past. Our simulation experiments suggest that high-cohesion communities are able to rapidly disseminate the initially optimal behavioral strategy compared to low-cohesion communities. This rapid and pervasive change, however, decreases behavioral diversity; i.e., once a high cohesion community settles on a strategy, most or all households adopt that behavior. Following environmental changes that reduce the efficacy of the initially optimal strategy, rendering it suboptimal relative to the alternative strategy, high-cohesion communities can fail to adapt. As a result, despite faring better early in the course of computational experiments, high-cohesion communities may ultimately experience worse outcomes. In the face of uncertainty in predicting future environmental stressors due to climate change, strategies to improve effective adaptation to optimal disease prevention strategies should balance between intervention efforts that promote protective behaviors based on current scientific understanding and the need to guard against the crystallization of inflexible norms. Developing generalizable models allows us to integrate a wide range of theories multiple datasets pertaining to the relationship between social mechanisms and adaptation, which can provide further understanding of future climate change impacts. Models such as the one we present can generate hypotheses about the mechanisms that underlie the dynamics of adaptation events and suggest specific points of measurement to assess the impact of these mechanisms. They can be incorporated as modules within predictive simulations for specific socio-ecological contexts.