Coupling hydrologic and infectious disease models to explain regional differences in schistosomiasis transmission in southwestern China

Assessing the nonhuman primate reservoir of Schistosoma mansoni in Africa: a systematic review

BACKGROUND

Reports of natural infections of Schistosoma mansoni in a number of species of nonhuman primates (NHPs) in Africa, coupled with the substantial overlap of NHP habitats and human schistosomiasis endemic areas, has led to concerns about the role of NHPs in the transmission of human schistosomiasis. We conducted a systematic review of the literature to describe the current scope of knowledge for Africa, for the NHP species implicated, their geographical distribution, infection rates with S. mansoni, and to discuss the implications for public health and conservation.

MAIN TEXT

A systematic search of the literature was performed using PubMed, Web of Science, Google Scholar, the World Health Organization (WHO) library database, World Cat, and ScienceDirect without any language restriction. Studies examining S. mansoni infection of any African NHP species were included. Study types, primate species, their geographical distribution, and parasite diagnostic techniques reported in the studies were qualitatively summarized. Data for species with sample sizes ≥10 were included in the meta-analysis. We assessed the reported infection rate, and used a random-effects model to estimate the summary infection rates and 95% confidence intervals (CIs). We assessed heterogeneity among studies using the I2 statistics. Twenty-nine publications, from 1960 to 2018, were identified and included in the review. The studies examined a total of 2962 primates belonging to 22 species in 11 genera across ten countries (Cameroon, Eritrea, Ethiopia, Gabon, Kenya, Nigeria, Senegal, Tanzania, Uganda, and Zimbabwe), and S. mansoni infections were found in nine species of five genera in all countries. When we excluded studies with sample sizes < 10, data from 24 studies on 11 species of primates in three genera in ten countries remained in the meta-analysis. The overall pooled estimate of infection rate was 10% (95% CI: 6-16%) with high heterogeneity (I2 = 94.77%) across countries and species/genera. Among the three genera, Pan had the highest infection rate of 15% (95% CI: 0-55%), followed by Papio at 11% (95% CI: 6-18%), and Cercopithecus at 5% (95% CI: 0-14%). The association between NHP and human infections was positive, but not significant, due to low study sample matches and high variation.

CONCLUSIONS

Our findings suggest that S. mansoni infection rate is high in African NHPs, with substantial heterogeneities across species/genera and countries in Africa. Given the evidence for potential spillover and spillback of S. mansoni between African NHPs and humans, further research is urgently needed to understand ecology and mechanisms of transmission of the parasite between NHP and human hosts, in order to inform control strategies of this important neglected tropical disease.

Role of ecological approaches to eliminating schistosomiasis in Eryuan County evaluated by system modelling

BACKGROUND

Schistosomiasis was severely prevalent in Yunnan Province, and it is difficult to achieve its elimination by convention approaches due to complexity of the nature. We explored the comprehensive model to eliminate schistosomiasis in Eryuan County, Yunnan Province, the People's Republic of China, through integration with the ecological protection programme in Erhai Lake, in order to promote an efficient elimination strategy. We expected that this model is able to be tailored to other local settings, which help achieve the goal of precisely eliminating the disease in Yunnan Province.

METHODS

Eryuan County of Yunnan Province was chosen as the study area, where the data on environmental protection activities in Erhai Lake and on the schistosomiasis control programme were collected through different departments of Erhai County government since 2015. System modelling was performed using system dynamics software to establish a simulation model in order to evaluate the effectiveness of intervention activities.

RESULTS

Ecological approaches to control schistosomiasis in Eryuan County consist of three major components: (i) implementing precise interventions to stop schistosomiasis transmission by means of controlling the source of infection, blocking the biological transmission chains and cutting off the route of disease transmission; (ii) employing ecological approaches to improve the co-effectiveness of environmental protection and schistosomiasis prevention in the study area; and (iii) strengthening the professional skills of personnel involving in the schistosomiasis control programme. Simulation results showed that this strategy could speed up the progress of schistosomiasis control programme moving from the control stage to the elimination stage.

CONCLUSIONS

Ecological approaches implemented in schistosomiasis endemic areas of the Eryuan region are able to improve the co-effectiveness of environmental protection and schistosomiasis control, providing a new avenue for eliminating schistosomiasis thanks to the application of precise interventions.

Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite-Based Observations in Detecting Outbreaks

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite-based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data-driven forecasts also effectively captured the increased vulnerability of dry-cold regions of the country, compared to the wet-warm regions.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.

Reaching the Surveillance-Response Stage of Schistosomiasis Control in The People's Republic of China: A Modelling Approach

With the goal set to eliminate schistosomiasis nationwide by 2020, The People's Republic of China has initiated the surveillance-response stage to identify remaining sources of infection and potential pockets from where the disease could reemerge. Shifting the focus from classical monitoring and evaluation to rapid detection and immediate response, this approach requires modelling to bridge the surveillance and response components. We review here studies relevant to schistosomiasis modelling in a Chinese surveillance-response system with the expectation to achieve a practically useful understanding of the current situation and potential future study directions. We also present useful experience that could tentatively be applied in other endemic regions in the world. Modelling is discussed at length as it plays an essential role, both with regard to the intermediate snail host and in the definitive, mammal hosts. Research gaps with respect to snail infection, animal hosts and sectoral research cooperation are identified and examined against the prevailing background of ecosystem and socioeconomic changes with a focus on coexisting challenges and opportunities in a situation with increasing financial constraints.

Estimating the Health Impact of Climate Change with Calibrated Climate Model Output

Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama.

A research agenda for helminth diseases of humans: modelling for control and elimination

Mathematical modelling of helminth infections has the potential to inform policy and guide research for the control and elimination of human helminthiases. However, this potential, unlike in other parasitic and infectious diseases, has yet to be realised. To place contemporary efforts in a historical context, a summary of the development of mathematical models for helminthiases is presented. These efforts are discussed according to the role that models can play in furthering our understanding of parasite population biology and transmission dynamics, and the effect on such dynamics of control interventions, as well as in enabling estimation of directly unobservable parameters, exploration of transmission breakpoints, and investigation of evolutionary outcomes of control. The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to review helminthiases research and identify research priorities and gaps. A research and development agenda for helminthiasis modelling is proposed based on identified gaps that need to be addressed for models to become useful decision tools that can support research and control operations effectively. This agenda includes the use of models to estimate the impact of large-scale interventions on infection incidence; the design of sampling protocols for the monitoring and evaluation of integrated control programmes; the modelling of co-infections; the investigation of the dynamical relationship between infection and morbidity indicators; the improvement of analytical methods for the quantification of anthelmintic efficacy and resistance; the determination of programme endpoints; the linking of dynamical helminth models with helminth geostatistical mapping; and the investigation of the impact of climate change on human helminthiases. It is concluded that modelling should be embedded in helminth research, and in the planning, evaluation, and surveillance of interventions from the outset. Modellers should be essential members of interdisciplinary teams, propitiating a continuous dialogue with end users and stakeholders to reflect public health needs in the terrain, discuss the scope and limitations of models, and update biological assumptions and model outputs regularly. It is highlighted that to reach these goals, a collaborative framework must be developed for the collation, annotation, and sharing of databases from large-scale anthelmintic control programmes, and that helminth modellers should join efforts to tackle key questions in helminth epidemiology and control through the sharing of such databases, and by using diverse, yet complementary, modelling approaches.

Model approaches for estimating the influence of time-varying socio-environmental factors on macroparasite transmission in two endemic regions

The environmental determinants of vector- and host-borne diseases include time-varying components that modify key transmission parameters, resulting in transient couplings between environmental phenomena and transmission processes. While some time-varying drivers are periodic in nature, some are aperiodic, such as those that involve episodic events or complex patterns of human behavior. Understanding these couplings can allow for prediction of periods of peak infection risk, and ultimately presents opportunities for optimizing intervention selection and timing. Schistosome macroparasites of humans exhibit multiple free-living stages as well as intermediate hosts, and are thus model organisms for illustrating the influence of environmental forcing on transmission. Time-varying environmental factors, termed gating functions, for schistosomes include larval response to temperature and rainfall, seasonal water contact patterns and snail population dynamics driven by weather variables. The biological bases for these modifiers are reviewed, and their values are estimated and incorporated into a transmission model that simulates a multi-year period in two schistosomiasis endemic regions. Modeling results combined with a scale dependent correlation analysis indicate the end effect of these site-specific gating functions is to strongly govern worm burden in these communities, in a manner particularly sensitive to the hydrological differences between sites. Two classes of gating functions were identified, those that act in concert to modify human infection (and determine worm acquisition late in the season), and those that act on snail infection (and determine early season worm acquisition). The importance of these factors for control programs and surveillance is discussed.

A 5-year longitudinal study of schistosomiasis transmission in Shian village, the Anning River Valley, Sichuan Province, the Peoples' Republic of China

Background

Schistosoma japonicum is a major public health concern in the Peoples' Republic of China (PRC), with over one million people infected and another 50 million living in areas at risk of infection. Based on ecological, environmental, population genetic and molecular factors, schistosomiasis transmission in PRC can be categorised into four discrete ecosystems or transmission modes. It is predicted that the Three Gorges Dam (TGD) will impact upon the transmission of schistosomiasis in the PRC, with varying degree across the four transmission modes. We undertook longitudinal surveillance from 2002 to 2006 in sentinel villages both above and below the TGD across five provinces (Hunan, Jiangxi, Hubei, Anhui and Sichuan) to determine whether there was any impact of the TGD on schistosomiasis transmission during its construction. Here we present the results from a schistosomiasis-endemic village located above the dam in Sichuan Province.

Results

Baseline results showed a human S. japonicum prevalence of 42.0% (95% CI: 36.6-47.5). At follow-up, results showed that the incidence of S. japonicum infection in the selected human cohort in Shian decreased by three quarters from 46% in 2003 to 11.3% in 2006. A significant (P < 0.01) downward trend was also evident in the yearly adjusted (for water contact) odds ratios. Over the four years of follow-up, the incidence of S. japonicum infection in bovines declined from 11.8% in the first year to zero in the final year of follow-up.

Conclusions

The substantial decrease in human (75%) and bovine (100%) incidence observed in Shian village can probably be attributed to the annual human and bovine PZQ treatment of positives; as seen in drug (PZQ) intervention studies in other parts of PRC. If an increase in schistosome transmission had occurred as a result of the TGD, it would be of negligible size compared to the treatment induced decline seen here. It appears therefore that the construction of the TGD had virtually no impact on schistosomiasis transmission in Shian village over the period of study. Furthermore, contrary to previous reports from Sichuan downplaying the role of animals in human schistosome transmission, bovines may indeed play a role.

Modelling environmentally-mediated infectious diseases of humans: transmission dynamics of schistosomiasis in China

Macroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments. Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed.

Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China

BACKGROUND

Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter.

RESULTS

The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%).

CONCLUSIONS

The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation.

Sustainable control of water-related infectious diseases: a review and proposal for interdisciplinary health-based systems research

OBJECTIVE

Even when initially successful, many interventions aimed at reducing the toll of water-related infectious disease have not been sustainable over longer periods of time. Here we review historical practices in water-related infectious disease research and propose an interdisciplinary public health oriented systems approach to research and intervention design.

DATA SOURCES

On the basis of the literature and the authors' experiences, we summarize contributions from key disciplines and identify common problems and trends. Practices in developing countries, where the disease burden is the most severe, are emphasized.

DATA EXTRACTION

We define waterborne and water-associated vectorborne diseases and identify disciplinary themes and conceptual needs by drawing from ecologic, anthropologic, engineering, political/economic, and public health fields. A case study examines one of the classes of water-related infectious disease.

DATA SYNTHESIS

The limited success in designing sustainable interventions is attributable to factors that include the complexity and interactions among the social, ecologic, engineering, political/economic, and public health domains; incomplete data; a lack of relevant indicators; and most important, an inadequate understanding of the proximal and distal factors that cause water-related infectious disease. Fundamental change is needed for research on water-related infectious diseases, and we advocate a systems approach framework using an ongoing evidence-based health outcomes focus with an extended time horizon. The examples and case study in the review show many opportunities for interdisciplinary collaborations, data fusion techniques, and other advances.

CONCLUSIONS

The proposed framework will facilitate research by addressing the complexity and divergent scales of problems and by engaging scientists in the disciplines needed to tackle these difficult problems. Such research can enhance the prevention and control of water-related infectious diseases in a manner that is sustainable and focused on public health outcomes.