Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal inference, it is classically of interest to investigate the counterfactuals. In the context of STIF, it is possible to use nowcasting to assess the possible counterfactual realization of disease in an incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models are discussed, and the importance of the ST component in nowcasting is assessed. Real examples of lockdowns for COVID-19 in two US states during 2020 and 2021 are provided. The degeneracy in prediction over longer time periods is highlighted, and the wide confidence intervals characterize the forecasts. For SC, the early and short lockdown contrasted with the longer NJ intervention. The approach here demonstrated marked differences in spatio-temporal disparities across counties with respect to an adherence to counterfactual predictions

Preliminary estimation of temporal and spatiotemporal dynamic measures of COVID-19 transmission in Thailand

Background As a new emerging infectious disease pandemic, there is an urgent need to understand the dynamics of COVID-19 in each country to inform planning of emergency measures to contain its spread. It is essential that appropriate disease control activities are planned and implemented in a timely manner. Thailand was one of the first countries outside China to be affected with subsequent importation and domestic spread in most provinces in the country. Method A key ingredient to guide planning and implementation of public health measures is a metric of transmissibility which represents the infectiousness of a disease. Ongoing policies can utilize this information to plan appropriately with updated estimates of disease transmissibility. Therefore we present descriptive analyses and preliminary statistical estimation of reproduction numbers over time and space to facilitate disease control activities in Thailand. ResultsThe estimated basic reproduction number for COVID-19 during the study ranged from 2.23–5.90, with a mean of 3.75. We also tracked disease dynamics over time using temporal and spatiotemporal reproduction numbers. The results suggest that the outbreak was under control since the middle of April. After the boxing stadium and entertainment venues, the numbers of new cases had increased and spread across the country. Discussion Although various scenarios about assumptions were explored in this study, the real situation was difficult to determine given the limited data. More thorough mathematical modelling would be helpful to improve the estimation of transmissibility metrics for emergency preparedness as more epidemiological and clinical information about this new infection becomes available. However, the results can be used to guide interventions directly and to help parameterize models to predict the impact of these interventions

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand

Background: Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures. Methods: We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand. Results: The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities. Conclusion: Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges

Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

Background In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. Methods In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. Results From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. Conclusions A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

Background: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. Methods: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. Results: In the simulation study, Getis Ord Gi∗ and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗ and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. Conclusions: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings

Spatiotemporal Epidemiology of Tuberculosis in Thailand from 2011 to 2020

Tuberculosis is a leading cause of infectious disease globally, especially in developing countries. Better knowledge of spatial and temporal patterns of tuberculosis burden is important for effective control programs as well as informing resource and budget allocation. Studies have demonstrated that TB exhibits highly complex dynamics in both spatial and temporal dimensions at different levels. In Thailand, TB research has been primarily focused on surveys and clinical aspects of the disease burden with little attention on spatiotemporal heterogeneity. This study aimed to describe temporal trends and spatial patterns of TB incidence and mortality in Thailand from 2011 to 2020. Monthly TB case and death notification data were aggregated at the provincial level. Age-standardized incidence and mortality were calculated; time series and global and local clustering analyses were performed for the whole country. There was an overall decreasing trend with seasonal peaks in the winter. There was spatial heterogeneity with disease clusters in many regions, especially along international borders, suggesting that population movement and socioeconomic variables might affect the spatiotemporal distribution in Thailand. Understanding the space-time distribution of TB is useful for planning targeted disease control program activities. This is particularly important in low- and middle-income countries including Thailand to help prioritize allocation of limited resources

Forest malaria and prospects for anti-malarial chemoprophylaxis among forest goers: findings from a qualitative study in Thailand

Background: Across the Greater Mekong Subregion, malaria remains a dangerous infectious disease, particularly for people who visit forested areas where residual transmission continues. Because vector control measures offer incomplete protection to forest goers, chemoprophylaxis has been suggested as a potential supplementary measure for malaria prevention and control. To implement prophylaxis effectively, additional information is needed to understand forest goers’ activities and their willingness to use malaria prevention measures, including prophylaxis, and how it could be delivered in communities. Drawing on in-depth interviews with forest goers and stakeholders, this article examines the potential acceptability and implementation challenges of malaria prophylaxis for forest goers in northeast Thailand. Methods: In-depth interviews were conducted with forest goers (n = 11) and stakeholders (n = 16) including healthcare workers, community leaders, and policymakers. Interviews were audio-recorded, transcribed and coded using NVivo, employing an inductive and deductive approach, for thematic analysis. Results: Forest goers were well aware of their (elevated) malaria risk and reported seeking care for malaria from local health care providers. Forest goers and community members have a close relationship with the forest but are not a homogenous group: their place and time-at-risk varied according to their activities and length of stay in the forest. Among stakeholders, the choice and cost of anti-malarial prophylactic regimen—its efficacy, length and complexity, number of tablets, potential side effects, and long-term impact on users—were key considerations for its feasibility. They also expressed concern about adherence to the preventive therapy and potential difficulty treating malaria patients with the same regimen. Prophylaxis was considered a low priority in areas with perceived accessible health system and approaching malaria elimination. Conclusions: In the context of multi-drug resistance, there are several considerations for implementing malaria prophylaxis: the need to target forest goers who are at-risk with a clear period of exposure, to ensure continued use of vector control measures and adherence to prophylactic anti-malarials, and to adopt an evidence-based approach to determine an appropriate regimen. Beyond addressing current intervention challenges and managing malaria incidence in low-transmission setting, it is crucial to keep malaria services available and accessible at the village level especially in areas home to highly mobile populations

Developments in clustering and surveillance for spatial health data

Relative risk estimation or disease mapping concern the global smoothing of risk and estimation of true underlying risk level. However, it is also appropriate to investigate association with the local properties of relative risk surface. These local properties include peaks of risk and local heterogeneity in risk, and cluster detection is often the main focus on local features of the risk surface where elevations or depression of risks happen. Cluster analysis of disease incidence has a long history, and a variety of approaches can be adopted for this analysis ranging from testing-based methods to fully parameterized cluster. Although a range of models available with a variety of goals in disease mapping applications focuses on retrospective analysis, prospective analyses are essential in many public health situations when timeliness is a key component. The importance of the early detection of unusual public health events is the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. There are two methods of detection: retrospective and prospective. A retrospective analysis is carried out for the whole dataset to decide on the presence of a change based on the information from the past. To detect changes prospectively, observations are added to the process and a decision is made whether to collect more data or declare as an outbreak. The later detection of changes is our focus of surveillance. The Centers for Disease Control and Prevention (CDC) defines an outbreak based on the number of cases occurring after an investigation of the disease. This definition is not adapted to the prospective analysis because an alarm should be triggered before the investigation and thus before the determination of a potential epidemiological link between cases. To assist public health practitioners to make the decision, statistical methods are adopted to assess unusual events on the fly. In this research plan a range of novel Bayesian spatial models and measures for disease cluster assessment and public health surveillance are proposed and evaluated. The general aims of the proposal are structured as follows: Aim 1: Evaluation of Cluster recovery for small area relative risk models. The analysis of disease risk is often considered via relative risk. The comparison of relative risk estimation methods with ‘true risk’ scenarios has been considered on various occasions. However, there has been little examination of how well competing methods perform when the focus is clustering of risk. In this paper, a simulated evaluation of a range of potential spatial risk models and a range of measures that can be used for a) cluster goodness-of-fit, b) cluster diagnostics, are considered. Results suggest that exceedence probability is a poor measure of hot spot clustering because of model dependence, whereas residual–based methods are less model dependent and perform better. Local deviance information criteria (Local DIC) measures perform well, but conditional predictive ordinate (CPO) measures yield a high false positive rate. Aim 2: Bayesian detection of small area health anomalies using Kullback – Leibler divergence. The importance of early detection of unusual health events depends on the ability to rapidly detect any substantial changes in disease, thus facilitate timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area data. We investigate the performance of the proposed surveillance technique and compare with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling using a simulation study. Finally, the detection methods are applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Aim 3: Prospective Bayesian surveillance for spatial case event data. There has been little development of surveillance procedures for epidemiological data with fine spatial resolution such as case events at residential address locations. This is often due to difficulties of access when confidentiality of medical records is an issue. However, when such data are available, it is important to be able to affect an appropriate analysis strategy. We propose a model for point events in the context of prospective surveillance based on conditional logistic modeling. A weighted conditional autoregressive model is developed for irregular lattices to account for distance effects, and a Dirichlet tessellation is adopted to define the neighborhood structure. Localized clustering diagnostics are compared including the proposed local Kullback-Leibler information criterion. A simulation study is conducted to examine the surveillance and detection methods, and a data example is provided of non-Hodgkin Lymphoma data in South Carolina

Integrated surveillance: Joint modeling of rodent and human tularemia cases in Finland

ObjectiveWe seek to integrate multiple streams of geo-coded information withthe aim to improve public health surveillance accuracy and efficiency.Specifically for vector-borne diseases, knowledge of spatial andtemporal patterns of vector distribution can help early prediction ofhuman incidence. To this end, we develop joint modeling approachesto evaluate the contribution of vector or reservoir information on earlyprediction of human cases. A case study of spatiotemporal modelingof tularemia human incidence and rodent population data from Finnishhealth care districts during the period 1995-2013 is provided. Resultssuggest that spatial and temporal information of rodent abundance isuseful in predicting human cases.IntroductionAn increasing number of geo-coded information streams areavailable with possible use in disease surveillance applications.In this setting, multivariate modeling of health and non-health dataallows assessment of concurrent patterns among data streams andconditioning on one another. Therefore it is appropriate to considerthe analysis of their spatial distributions together. Specifically forvector-borne diseases, knowledge of spatial and temporal patternsof vector distribution could inform incidence in humans. Tularemiais an infectious disease endemic in North America and parts ofEurope. In Finland tularemia is typically mosquito-transmitted withrodents serving as a host; however a country-wide understanding ofthe relationship between rodents and the disease in humans is stilllacking. We propose a methodology to help understand the associationbetween human tularemia incidence and rodent population levels.MethodsData on rodent population levels are collected around the countryby the Finnish Natural Resources Institute. Human Tularaemia casesare recorded as laboratory-confirmed and reported to the NationalInfectious Disease Register (NIDR). Human cases and rodent datawere aggregated to match the 20 Finnish health districts over the period1995-2013 [1]. We develop our methodology in a Bayesian setting.The counts of human cases for each health district in a given yearare assumed to follow a Poisson distribution and the rodent data areassumed to have a categorical likelihood. The linear predictors linkedto the human and rodent likelihood functions are then decomposedadditively into spatial, temporal, and space-time interaction randomeffects. We then link the two likelihoods via the interaction term byassuming that the human spatiotemporal variation is dependent on therodent activity with one-year lag. In the case of the rodent data, wealso included two additional spatial and non-spatial contextual termsto better model ecological effects associated with rodent populationlevels as described before [2]. We then finally develop indicators, onthe scale 0 to 1, to quantify the association between human incidenceand a rodent vector.ResultsResults suggest that spatial and temporal information of rodentabundance is useful in predicting human cases.ConclusionsFuture modeling directions are recommended to includeenvironmental and epidemiological factors. To the best of ourknowledge, this is the first time that rodent data, captured for non-health related purposes, is used to better inform the human risk oftularemia in Finland

Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal inference, it is classically of interest to investigate the counterfactuals. In the context of STIF, it is possible to use nowcasting to assess the possible counterfactual realization of disease in an incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models are discussed, and the importance of the ST component in nowcasting is assessed. Real examples of lockdowns for COVID-19 in two US states during 2020 and 2021 are provided. The degeneracy in prediction over longer time periods is highlighted, and the wide confidence intervals characterize the forecasts. For SC, the early and short lockdown contrasted with the longer NJ intervention. The approach here demonstrated marked differences in spatio-temporal disparities across counties with respect to an adherence to counterfactual predictions