Feasible intervention combinations for achieving a safe exit of the Zero-COVID policy in China and its determinants: an individual-based model study.

BACKGROUND: Although several pathways have been proposed as the prerequisite for a safe phase-out in China, it is not clear which of them are the most important for keeping the mortality rate low, what thresholds should be achieved for these most important interventions, and how the thresholds change with the assumed key epidemiological parameters and population characteristics. METHODS: We developed an individual-based model (IBM) to simulate the transmission of the Omicron variant in the synthetic population, accounting for the age-dependent probabilities of severe clinical outcomes, waning vaccine-induced immunity, increased mortality rates when hospitals are overburdened, and reduced transmission when self-isolated at home after testing positive. We applied machine learning algorithms on the simulation outputs to examine the importance of each intervention parameter and the feasible intervention parameter combinations for safe exits, which is defined as having mortality rates lower than that of influenza in China (14.3 per 100, 000 persons). RESULTS: We identified vaccine coverage in those above 70 years old, number of ICU beds per capita, and the availability of antiviral treatment as the most important interventions for safe exits across all studied locations, although the thresholds required for safe exits vary remarkably with the assumed vaccine effectiveness, as well as the age structure, age-specific vaccine coverage, community healthcare capacity of the studied locations. CONCLUSIONS: The analytical framework developed here can provide the basis for further policy decisions that incorporate considerations about economic costs and societal impacts. Achieving safe exits from the Zero-COVID policy is possible, but challenging for China's cities. When planning for safe exits, local realities such as the age structure and current age-specific vaccine coverage must be taken into consideration.

Exploring the Local Determinants of SARS-CoV-2 Transmission and Control via an Exposure-Based Model.

A simulation model was developed aimed at assisting local public health authorities in exploring strategies for the suppression of SARS-CoV-2 transmission. A mechanistic modeling framework is utilized based on the daily airborne exposure of individuals defined in terms of inhaled viruses. Comparison of model outputs and observed data confirms that the model can generate realistic patterns of secondary cases. In the example investigated, the highest risk of being newly infected was among young adults, males, and people living in large households. Among risky occupations are food preparation and serving, personal care and service, sales, and production-related occupations. Results also show a pattern consistent with superspreading with 70% of initial cases who do not transmit at all while 13.4% of primary cases contribute 80% of secondary cases. The impacts of school closure and masking on the synthetic population are very small, but for students, school closure resulted in more time at home and increased secondary cases among them by over 25%. Requiring masks at schools decreased the case count by 80%. We conclude that the simulator can be useful in exploring local intervention scenarios and provides output useful in assessing the confidence that might be placed on its predictions.

Preliminary genetic evidence of two different populations of Opisthorchis viverrini in Lao PDR.

Opisthorchis viverrini is a major public health concern in Southeast Asia. Various reports have suggested that this parasite may represent a species complex, with genetic structure in the region perhaps being dictated by geographical factors and different species of intermediate hosts. We used four microsatellite loci to analyze O. viverrini adult worms originating from six species of cyprinid fish in Thailand and Lao PDR. Two distinct O. viverrini populations were observed. In Ban Phai, Thailand, only one subgroup occurred, hosted by two different fish species. Both subgroups occurred in fish from That Luang, Lao PDR, but were represented to very different degrees among the fish hosts there. Our data suggest that, although geographical separation is more important than fish host specificity in influencing genetic structure, it is possible that two species of Opisthorchis, with little interbreeding, are present near Vientiane in Lao PDR.

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.

Parameter estimation and site-specific calibration of disease transmission models.

The use of mathematical models for developing management options for controlling infectious diseases at alocal scale requires that the structure and parameters of the model reflect the realities of transmission at that scale. Data available to inform local models are generally sparse and come from diverse sources and in diverse formats. These characteristics of the data and the complex structure of transmission models, result in many different parameter sets which mimic the local behavior of the system to within the resolution of field data, even for a model of fixed structure. A Bayesian approach is described, at both a practical and a theoretical level, which involves the assignment of prior parameter distributions and the definition of a semi-quantitative goodness of fit criteria which are essentially priors on the observable outputs. Monte Carlo simulations are used to generate samples from the posterior parameter space. This space is generally much more constrained than the prior space, but with a highly complex multivariate structure induced by the mathematical model. In applying the approach to a model of schistosomiasis transmission in a village in southwestern China, calibration of the model was found to be sensitive to the effective reproductive number, R(eff). This finding has implications both for computation time for the Monte Carlo analysis and for the specification of field data to efficiently calibrate the model for transmission control.

[Modeling transmission dynamics and control of schistosomiasis in the mountainous region, Sichuan].

OBJECTIVE: To use a mathematical model and computer simulation to study transmission dynamics and control of schistosomiasis in mountainous regions of Sichuan. METHODS: Based on studies of schistosomiasis japonica transmission in 20 villages in mountainous regions of Sichuan, a mathematical model was developed to characterize the impact of local environmental factors on transmission intensity. The model integrated site-specific factors and was calibrated to field epidemiological data from 3 subset villages. The dichotomic method was then used to predict different control measures. RESULTS: The study showed high variations in prevalence of infection and infection intensity across villages, ranging between 3%-73%, 0.1-100 epg (eggs per gram stool), respectively. Important factors including occupation of local residents, exposure to contaminated water, microclimatic characteristics were integrated in the model. The predictions of dichotomic models showed that continuing chemotherapy (coverage between 50%-60%) could reduce infection intensities to 30%-80%, but could not change local transmission potential; therefore, the termination of chemotherapy would be followed by bouncing back of transmission. Sustaining targeted environmental interventions through snail and parasite oval control at certain coverage (30%-50%, respectively) could reduce the transmission to relatively stable levels. The model predictions showed that an integrated control (e.g., including both chemotherapy and environmental interventions) could suppress the transmission to an undetectable level even interruption of transmission between 5-10 years. CONCLUSION: The study demonstrated the feasibility of using a dynamic model, calibrated to local data, to gain insights into complicated processes underlying the transmission and informing site-specific control strategies.

Modeling liver fluke transmission in northeast Thailand: Impacts of development, hydrology, and control.

Human infection with the Southeast Asian liver fluke Opisthorchis viverrini and liver fluke-associated cholangiocarcinoma cause significant disease burden in Southeast Asia. While there has been considerable work to understand liver fluke pathology and to reduce infection prevalence, there remains a limited understanding of the environmental determinants of parasite transmission dynamics to inform treatment and control programs. A particular setting where targeted control efforts have taken place is the Lawa Lake complex in northeast Thailand. Here, we describe the recent history of host infections, as well as the hydrologic characteristics of this floodplain ecosystem that influence the extent of snail habitat and fish mobility and the transport of human waste and parasite cercariae. Using mathematical modeling, we outline a framework for reconstructing environmental transmission of O. viverrini over the course of the Lawa Project control program from its inception in 2008 until 2016, using locally acquired but fragmentary longitudinal infection data for both humans and environmental hosts. The role of water flow in facilitating movement between snail, fish, human, and reservoir hosts is a particular focus with respect to its relevant scales and its impact on success of interventions. In this setting, we argue that an understanding of the key environmental drivers of disease transmission processes is central to the effectiveness of any environmental intervention.

Review of "Mathematical Models for Neglected Tropical Diseases: Essential Tools for Control and Elimination, Part B" Edited by Maria-Gloria Basáñez and Roy M. Anderson.

BOOK DETAILS: Basáñez MG, Anderson RM, Editors: Mathematical Models for Neglected Tropical Diseases: Essential Tools for Control and Elimination, Part B, Volume 94, Advances in Parasitology, Academic Press; 2016, 430 pages. ISBN: 978-0-12-809971-1.

The interplay of climate, intervention and imported cases as determinants of the 2014 dengue outbreak in Guangzhou.

Dengue is a fast spreading mosquito-borne disease that affects more than half of the population worldwide. An unprecedented outbreak happened in Guangzhou, China in 2014, which contributed 52 percent of all dengue cases that occurred in mainland China between 1990 and 2015. Our previous analysis, based on a deterministic model, concluded that the early timing of the first imported case that triggered local transmission and the excessive rainfall thereafter were the most important determinants of the large final epidemic size in 2014. However, the deterministic model did not allow us to explore the driving force of the early local transmission. Here, we expand the model to include stochastic elements and calculate the successful invasion rate of cases that entered Guangzhou at different times under different climate and intervention scenarios. The conclusion is that the higher number of imported cases in May and June was responsible for the early outbreak instead of climate. Although the excessive rainfall in 2014 did increase the success rate, this effect was offset by the low initial water level caused by interventions in late 2013. The success rate is strongly dependent on mosquito abundance during the recovery period of the imported case, since the first step of a successful invasion is infecting at least one local mosquito. The average final epidemic size of successful invasion decreases exponentially with introduction time, which means if an imported case in early summer initiates the infection process, the final number infected can be extremely large. Therefore, dengue outbreaks occurring in Thailand, Singapore, Malaysia and Vietnam in early summer merit greater attention, since the travel volumes between Guangzhou and these countries are large. As the climate changes, destroying mosquito breeding sites in Guangzhou can mitigate the detrimental effects of the probable increase in rainfall in spring and summer.

Experimental and modelling investigations of Opisthorchis viverrini miracidia transmission over time and across temperatures: implications for control.

Transmissibility is a significant factor in parasite fitness. The rate and magnitude of parasite transmission affect prevalence and infection intensity in individual hosts and are influenced by environmental factors. In this context, the objectives of this study were: (i) to experimentally assess Opisthorchis viverrini miracidia survival and infectivity over time and across temperatures; and (ii) to combine these experimental results with environmental data to build a key component of a transmission model, identifying seasonal windows of transmission risk in hyper-endemic northeastern Thailand. Five replicates of 50 O. viverrini eggs were randomly distributed and maintained under four temperature conditions (25°C, 30°C, 35°C, 40°C). Microscopic observations were performed on all experimental units over a period of 3months to record miracidia motility and mortality trends. Six infection trials were also conducted to assess infectivity of miracidia over time and across temperatures, using observations of egg hatching success and infection rates. Upon completion of experiments, data were integrated into a transmission model to create a transmission risk index and to simulate seasonal transmission risk. Miracidia survival rate and motility decreased steadily with 50% mortality observed after 2weeks. Hatching and infection success also decreased significantly after 3weeks. Temperatures over 30°C were associated with increased mortality and decreased infectivity. When incorporating local environmental parameters into our model, we observed low transmission risk during the dry season and increasing transmission risk at the onset of the rainy season, culminating with the highest risk in September. We believe that our results provide the first estimates of O. viverrini miracidia survival and transmission potential under variable temperature conditions and suggest that high temperature treatment (>40°C) of fecal waste could be an efficient control strategy.

Exposure versus Susceptibility as Alternative Bases for New Approaches to Surveillance for Schistosoma japonicum in Low Transmission Environments.

Currently, schistosomiasis in China provides an excellent example of many of the challenges of moving from low transmission to the elimination of transmission for infectious diseases generally. In response to the surveillance dimension of these challenges, we here explore two strategic approaches to inform priorities for the development of improved methods addressed specifically to schistosomiasis in the low transmission environment. We utilize an individually-based model and the exposure data used earlier to explore surveillance strategies, one focused on exposure assessment and the second on our estimates of variability in individual susceptibility in the practical context of the current situation in China and the theoretical context of the behavior of transmission dynamics near the zero state. Our findings suggest that individual susceptibility is the major single determinant of infection intensity in both the low and medium risk environments. We conclude that there is considerable motivation to search for a biomarker of susceptibility to infection in humans, but that there would also be value in a method for monitoring surface waters for the free-swimming forms of the parasite in endemic or formerly endemic environments as an early warning of infection risk.

Climate and the Timing of Imported Cases as Determinants of the Dengue Outbreak in Guangzhou, 2014: Evidence from a Mathematical Model.

As the world's fastest spreading vector-borne disease, dengue was estimated to infect more than 390 million people in 2010, a 30-fold increase in the past half century. Although considered to be a non-endemic country, mainland China had 55,114 reported dengue cases from 2005 to 2014, of which 47,056 occurred in 2014. Furthermore, 94% of the indigenous cases in this time period were reported in Guangdong Province, 83% of which were in Guangzhou City. In order to determine the possible determinants of the unprecedented outbreak in 2014, a population-based deterministic model was developed to describe dengue transmission dynamics in Guangzhou. Regional sensitivity analysis (RSA) was adopted to calibrate the model and entomological surveillance data was used to validate the mosquito submodel. Different scenarios were created to investigate the roles of the timing of an imported case, climate, vertical transmission from mosquitoes to their offspring, and intervention. The results suggested that an early imported case was the most important factor in determining the 2014 outbreak characteristics. Precipitation and temperature can also change the transmission dynamics. Extraordinary high precipitation in May and August, 2014 appears to have increased vector abundance. Considering the relatively small number of cases in 2013, the effect of vertical transmission was less important. The earlier and more frequent intervention in 2014 also appeared to be effective. If the intervention in 2014 was the same as that in 2013, the outbreak size may have been over an order of magnitude higher than the observed number of new cases in 2014.The early date of the first imported and locally transmitted case was largely responsible for the outbreak in 2014, but it was influenced by intervention, climate and vertical transmission. Early detection and response to imported cases in the spring and early summer is crucial to avoid large outbreaks in the future.

Genetic and household risk factors for Schistosoma japonicum infection in the presence of larger scale environmental differences in the mountainous transmission areas of China.

Schistosoma japonicum egg excretion and kinship relationship data from 13 endemic villages in the mountainous transmission area near Xichang, in Sichuan province, China, were analyzed via a variance components methodology to assess the relative contribution of kinship, shared household, and shared village to the risk of infection. Large intervillage differences in egg counts exist in this region due to differences in transmission potential related to environmental differences in snail density and agricultural practices. After accounting for these intervillage differences, there was no kinship or household effect on egg excretion. This reinforces earlier findings that suggest environmental factors dominate risk in this region.

Exploring the contribution of host susceptibility to epidemiological patterns of Schistosoma japonicum infection using an individual-based model.

We recently reported the analysis of epidemiological data suggesting variability in individual susceptibility to infection by Schistosoma japonicum among rural villagers who reside in Sichuan Province of southwestern China. By supplementing the data used in the earlier analysis from other studies we have reported from this region, we presented improved estimates of cercarial exposure, which in turn, result in stronger evidence of susceptibility. This analysis was conducted using an individual-based mathematical model (IBM) whose use was motivated by the nature and extent of field data from the low-transmission environments exemplified by one of our datasets and typical of the current situation in most endemic areas of China. In addition to individual susceptibility and water contact, the model includes stochastic aspects of cercarial exposure as well as of diagnostic procedures, the latter being particularly relevant to the low-transmission environment. The simulation studies show that, to produce key aspects of the epidemiological findings, the distribution of susceptibility ranges over several orders of magnitude and is highly right skewed. We found no compelling evidence that the distribution of susceptibility differed between the two populations that underlie both the epidemiological and simulation results.

Associations between schistosomiasis and the use of human waste as an agricultural fertilizer in China.

BACKGROUND: Human waste is used as an agricultural fertilizer in China and elsewhere. Because the eggs of many helminth species can survive in environmental media, reuse of untreated or partially treated human waste, commonly called night soil, may promote transmission of human helminthiases. METHODOLOGY/PRINCIPAL FINDINGS: We conducted an open cohort study in 36 villages to evaluate the association between night soil use and schistosomiasis in a region of China where schistosomiasis has reemerged and persisted despite control activities. We tested 2,005 residents for Schistosoma japonicum infection in 2007 and 1,365 residents in 2010 and interviewed heads of household about agricultural practices each study year. We used an intervention attributable ratio framework to estimate the association between night soil use and S. japonicum infection. Night soil use was reported by half of households (56% in 2007 and 46% in 2010). Village night soil use was strongly associated with human S. japonicum infection in 2007. We estimate cessation of night soil use would lead to a 49% reduction in infection prevalence in 2007 (95% CI: 12%, 71%). However, no association between night soil and schistosomiasis was observed in 2010. These inconsistent findings may be due to unmeasured confounding or temporal shifts in the importance of different sources of S. japonicum eggs on the margins of disease elimination. CONCLUSIONS/SIGNIFICANCE: The use of untreated or partially treated human waste as an agricultural fertilizer may be a barrier to permanent reductions in human helminthiases. This practice warrants further attention by the public health community.

Disease transmission models for public health decision making: toward an approach for designing intervention strategies for Schistosomiasis japonica.

Mathematical models of disease transmission processes can serve as platforms for integration of diverse data, including site-specific information, for the purpose of designing strategies for minimizing transmission. A model describing the transmission of schistosomiasis is adapted to incorporate field data typically developed in disease control efforts in the mountainous regions of Sichuan Province in China, with the object of exploring the feasibility of model-based control strategies. The model is studied using computer simulation methods. Mechanistically based models of this sort typically have a large number of parameters that pose challenges in reducing parametric uncertainty to levels that will produce predictions sufficiently precise to discriminate among competing control options. We describe here an approach to parameter estimation that uses a recently developed statistical procedure called Bayesian melding to sequentially reduce parametric uncertainty as field data are accumulated over several seasons. Preliminary results of applying the approach to a historical data set in southwestern Sichuan are promising. Moreover, technologic advances using the global positioning system, remote sensing, and geographic information systems promise cost-effective improvements in the nature and quality of field data. This, in turn, suggests that the utility of the modeling approach will increase over time.

Spatial and temporal variability in schistosome cercarial density detected by mouse bioassays in village irrigation ditches in Sichuan, China.

A mouse bioassay was used monthly over the infection season of 2001 to determine the temporal and spatial variability of schistosome cercarial density in irrigation ditches in five villages in southwestern Sichuan Province in the People's Republic of China. Analysis of variance showed that approximately half of the variability was due to the village and site within the village, with little contribution from air temperature, weekly average rainfall, or the month within the infection season in which the bioassay was performed. The location-specific variability in these data suggest that epidemiologic studies will generally have low power to detect the influence of water-contact intensity on human parasite burden without taking account of variations in cercarial density at sites of water contact.

Factors influencing the transmission of Schistosoma japonicum in the mountains of Sichuan Province of China.

Twenty villages in the Anning River Valley of southwestern Sichuan China were surveyed for Schistosoma japonicum infections in humans and domestic animals. Also surveyed were human water contact patterns, snail populations, cercarial risk in irrigation systems, and agricultural land use. Few animals were infected, while village prevalence of infection in humans ranged from 3% to 68% and average village eggs per gram of stool ranged from 0 to 110. Except for occupation and education, individual characteristics were not strong determinants of infection intensity within a village. Differences in human infection intensity between these villages are strongly associated with crop type, with low-intensity villages principally growing rice, in contrast to villages devoting more land to vegetables and tobacco. Cercarial risk in village irrigation systems is associated with snail density and human infection intensity through the use of manure-based fertilizer. Some of the agricultural and environmental factors associated with infection risk can be quantified using remote sensing technology.

A quantitative framework for a multi-group model of Schistosomiasis japonicum transmission dynamics and control in Sichuan, China.

A quantitative framework is presented for the site-specific characterization of schistosomiasis transmission with the object of developing local control strategies. Central to the framework is a worm-burden model using ordinary differential equations of disease transmission in risk groups defined by residence and occupation. The model incorporates temperature- and precipitation-dependent seasonality of infectious stages, snail population dynamics, and seasonal patterns of human water contact specific to the local agricultural setting. The model's parameters are separated into two main subsets, those associated with the general biology of the parasite and its life cycle in the human and the snail and those associated with directly measurable features of disease status in the local population or relevant aspects of the local environment. In this regard, the model is structured and parameterized to take maximum advantage of data that can be collected in rural China by conventional methods. For example, it includes a statistical model for egg excretion to the environment by each risk group which is based on local population surveys of the prevalence and intensity of infection. The second element of the framework of analysis relates to the strategy for parameter estimation and calibration to local conditions. We propose a Bayesian approach in which parameter estimates are refined over time by methods employing extensive computer simulations. An early analysis of data collected between 1987 and 1989 in endemic villages near Xichang City in southwestern Sichuan provides encouragement that parametric uncertainty can be reduced to levels adequate to explore effective control strategies.