Computational modeling to address the burden of influenza and strategies of control measures in Thailand

Influenza is one of re-emerging infectious disease in Thailand. The true burden of influenza is not known and is needed for influenza preparedness. Thailand has a vaccine policy targets at healthcare worker (HCWs), people aged 6-24 months or ≥65 years, people with chronic medical condition (CMC), and pregnant women. However, amount of vaccine is limited and policy planners need information for vaccine prioritization. The government also promote non-pharmaceutical interventions, but their impact is not well studied. This research aimed to use agent-based model (ABM) to estimate influenza burden in Thailand and assess impact of control measures. The basic reproductive number (R0) based on Thailand's context is unknown and should be estimated for further studies of influenza dynamics. The R0 was estimated using formula relating the epidemic growth rate (r) and generation time. The projection of influenza burden was studied by fitting an ABM. The model contains a 58,354,744 synthetic Thai population, incorporates people with CMC and HCWs. At start, 100 agents were randomly assigned for initial infection. The model simulated the interactions of individuals with others over 180 days. Impacts of influenza vaccine were simulated at 50%, 75% and 100% coverage. Impacts of face mask wearing and hand washing were simulated at 10%, 25%, 50%, 75% and 100% coverage. The R0 estimates ranged from 1.11 to 1.77 (median 1.39). The highest attack rate occurs in school-age children and adolescents (15.32%). One Hundred percent coverage of target population policy can avoid morbidity and mortality by 47.06% and 59.61% in total population respectively. However, the benefit is very small for HCWs (3.75% case reduction). The extended policy to include children aged 2-18 years old can avoid >99% of cases. For non-pharmaceutical interventions, at least 50% compliance of the combined face mask use and hand washing policy can avoid morbidity and mortality >98% for all adherence of mask wearing. The public health significance of this research is that it provided information for health policy makers to guide optimized target population for vaccine, and to encourage non-pharmaceutical interventions for controlling influenza outbreak

Non-linear effect of different humidity types on scrub typhus occurrence in endemic provinces, Thailand.

BACKGROUND: Reported monthly scrub typhus (ST) cases in Thailand has an increase in the number of cases during 2009-2014. Humidity is a crucial climatic factor for the survival of chiggers, which is the disease vectors. The present study was to determine the role of humidity in ST occurrence in Thailand and its delayed effect. METHODS: We obtained the climate data from the Department of Meteorology, the disease data from Ministry of Public Health. Negative binomial regression combined with a distributed lag non-linear model (NB-DLNM) was employed to determine the non-linear effects of different types of humidity on the disease. This model controlled overdispersion and confounder, including seasonality, minimum temperature, and cumulative total rainwater. RESULTS: The occurrence of the disease in the 6-year period showed the number of cases gradually increased summer season (Mid-February - Mid-May) and then reached a plateau during the rainy season (Mid-May - Mid-October) and then steep fall after the cold season (Mid-October - Mid-February). The high level (at 70%) of minimum relative humidity (RHmin) was associated with a 33% (RR 1.33, 95% CI 1.13-1.57) significant increase in the number of the disease; a high level (at 14 g/m3) of minimum absolute humidity (AHmin) was associated with a 30% (RR 1.30, 95% CI 1.14-1.48); a high level (at 1.4 g/kg) of minimum specific humidity (SHmin) was associated with a 28% (RR 1.28, 95% CI 1.04-1.57). The significant effects of these types of humidity occurred within the past month. CONCLUSION: Humidity played a significant role in enhancing ST cases in Thailand, particularly at a high level and usually occurred within the past month. NB-DLNM had good controlled for the overdispersion and provided the precise estimated relative risk of non-linear associations. Results from this study contributed the evidence to support the Ministry of Public Health on warning system which might be useful for public health intervention and preparation in Thailand

Poultry-handling Practices during Avian Influenza Outbreak, Thailand

With poultry outbreaks of avian influenza H5N1 continuing in Thailand, preventing human infection remains a priority. We surveyed residents of rural Thailand regarding avian influenza knowledge, attitudes, and practices. Results suggest that public education campaigns have been effective in reaching those at greatest risk, although some high-risk behavior continues

Low Frequency of Infection with Avian Influenza Virus (H5N1) among Poultry Farmers, Thailand, 2004

In Thai provinces where avian influenza outbreaks in poultry had been confirmed in the preceding 6 months, serum from 322 poultry farmers was tested for antibodies to avian influenza virus subtype H5N1 by microneutralization assay. No study participant met the World Health Organization serologic criteria for confirmed infection

Seroprevalence of Antibodies to Avian Influenza Virus A (H5N1) among Residents of Villages with Human Cases, Thailand, 20051

No evidence of influenza virus A (H5N1) neutralizing antibodies was found in residents of 4 villages where human cases had occurred the previous year

Economic Value of Dengue Vaccine in Thailand

With several candidate dengue vaccines under development, this is an important time to help stakeholders (e.g., policy makers, scientists, clinicians, and manufacturers) better understand the potential economic value (cost-effectiveness) of a dengue vaccine, especially while vaccine characteristics and strategies might be readily altered. We developed a decision analytic Markov simulation model to evaluate the potential health and economic value of administering a dengue vaccine to an individual (≤ 1 year of age) in Thailand from the societal perspective. Sensitivity analyses evaluated the effects of ranging various vaccine (e.g., cost, efficacy, side effect), epidemiological (dengue risk), and disease (treatment-seeking behavior) characteristics. A ≥ 50% efficacious vaccine was highly cost-effective [< 1× per capita gross domestic product (GDP) ($4,289)] up to a total vaccination cost of$60 and cost-effective [< 3× per capita GDP ($12,868)] up to a total vaccination cost of$200. When the total vaccine series was $1.50, many scenarios were cost saving

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions

Large scale detailed mapping of dengue vector breeding sites using street view images.

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale