Optimising predictive modelling of Ross River virus using meteorological variables

BackgroundStatistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/Principal findingsWe developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model’s ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/SignificanceWe demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.     

Focus: Zoonotic Disease: Health System Contact and Awareness of Zoonotic Diseases: Can it Serve as One Health Entry Point in the Urban Community of Ahmedabad, India?

One Health (OH) is emphasized globally to tackle the (re)emerging issues at the human-animal-ecosystem interface. However, the low awareness about zoonoses remain a challenge in global south, thus this study documented the health system contact and its effect on the awareness level of zoonoses in the urban community of Ahmedabad, India. A community-based household survey was conducted between October 2018 and July 2019. A total of 460 households (HHs) were surveyed from two zones and 23 wards of the city through cluster sampling. A structured, pilot-tested, and researcher-administered questionnaire in the vernacular language was used to collect the information on demographic details, socio-economic details, health-seeking behavior for both the humans and their animals, human and animal health system contact details and the participants’ awareness on selected zoonotic diseases based on the prioritization (rabies, brucellosis, swine flu, and bird flu). Out of 460 surveyed households, 69% of HHs and 59% of HHs had a health system contact to the human and animal health system respectively at the community level. There are multiple health workers active on the community level that could potentially serve as One Health liaisons. The investigation of the knowledge and awareness level of selected zoonotic diseases revealed that 58.5%, 47.6%, and 4.6% know about rabies, swine and/or bird flu, and brucellosis, respectively. The mixed-effect linear regression model indicates that there is no significant effect on the zoonotic disease awareness score with the human health system contact; however, a minimal positive effect with the animal health system contact was evident.     

Improving probabilistic infectious disease forecasting through coherence

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.     

Global Disease Monitoring and Forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.     

Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases. We simulate 10 years of epidemics (2003–2012) based on mixing patterns derived from individual time-use data to show that the mixing patterns in 2009 yield the lowest number of total infections relative to if the epidemic had occurred in any of the other nine years. The World Health Organization and other public health bodies have emphasized an important role for ‘distancing’ or non-pharmaceutical interventions. Our empirical results suggest that neglect for voluntary avoidance behaviour in epidemic models may overestimate the public health benefits of public social distancing policies.     

Forecasting the 2013–2014 Influenza Season Using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.     

Use of Hangeul Twitter to Track and Predict Human Influenza Infection

Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.     

Burden of Disease Measured by Disability-Adjusted Life Years and a Disease Forecasting Time Series Model of Scrub Typhus in Laiwu,China

Background

Laiwu District is recognized as a hyper-endemic region for scrub typhus in Shandong Province, but the seriousness of this problem has been neglected in public health circles.

Methodology/Principal Findings

A disability-adjusted life years (DALYs) approach was adopted to measure the burden of scrub typhus in Laiwu, China during the period 2006 to 2012. A multiple seasonal autoregressive integrated moving average model (SARIMA) was used to identify the most suitable forecasting model for scrub typhus in Laiwu. Results showed that the disease burden of scrub typhus is increasing yearly in Laiwu, and which is higher in females than males. For both females and males, DALY rates were highest for the 60–69 age group. Of all the SARIMA models tested, the SARIMA(2,1,0)(0,1,0)₁₂ model was the best fit for scrub typhus cases in Laiwu. Human infections occurred mainly in autumn with peaks in October.

Conclusions/Significance

Females, especially those of 60 to 69 years of age, were at highest risk of developing scrub typhus in Laiwu, China. The SARIMA (2,1,0)(0,1,0)₁₂ model was the best fit forecasting model for scrub typhus in Laiwu, China. These data are useful for developing public health education and intervention programs to reduce disease.     

Improving real-time forecasting of water quality indicators with combination of process-based models and data assimilation technique

Water quality indicators can be used to characterize the status and quantify and qualify the change of aquatic ecosystems under different disturbance regimes. Although many studies have been done to develop and assess indicators and discuss interactions among them, few studies have focused on how to improve the predicted indicators and explore their variations in receiving water bodies. Accurate and effective predictions of ecological indictors are critical to better understand changes of water quality in aquatic ecosystems, especially for the real-time forecasting. Process-based water quality models can predict the spatiotemporal variations of the water quality indicators and provide useful information for policy-makers on sound management of water resources. Given their inherent constraints, however, such process models alone cannot actually guarantee perfect results since water quality models generally have a large number of parameters and involve many processes which are too complex to be efficiently calibrated. To overcome these limitations and explore a fast and efficient forecasting method for the change of water quality indictors, we proposed a new framework which combines the process-based models and data assimilation technique. Unlike most traditional approaches in which only the model parameters or initial conditions are updated or corrected and the models are run online, this framework allows the information extracted from observations and outputs of process models to be directly used in a data-driven local/modified local model. The results from the data-driven model are then assimilated into the original process model to further improve its forecasting ability. This approach can be efficiently run offline to directly correct and update the output of water quality models. We applied this framework in a real case study in Singapore. Two of the water quality indicators, namely salinity and oxygen were selected and tested against the observations, suggesting that a good performance of improving the model results and reducing computation time can be obtained. This approach is simple and efficient, especially suitable for real-time forecasting systems. Thus, it can enhance forecasting of water quality indictors and thereby facilitate the effective management of water resources.     

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles

BackgroundWith enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare.Methods and findingsWe introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data.ConclusionsThis study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.     

Accurate influenza forecasts using type-specific incidence data for small geographic units

Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.     

甲型H1N1流感病毒DNA序列碱基的预测

甲型流感H1N1亚型曾给人类带来了重大灾难。本文提出了一种利用时间序列模型预测碱基的方法,对所选取的1970年～2010年同源性相对较高的41条H1N1流感病毒数据利用ARIMA(p,d,q)模型对前20个位置去拟合并且预测,除极个别外由预报区域图显示原始数据都在预报区域内,表明模型建立的比较合理,预报效果很好,这对H1N1病毒的研究有着重要的意义。     

Addressing delayed case reporting in infectious disease forecast modeling

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden.In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.     

Public communication, risk perception, and the viability of preventive vaccination against communicable diseases

Because of the nature of preventive vaccination programs, the viability of these public health interventions is particularly susceptible to public perceptions. This is because vaccination relies on a concept of 'herd immunity', achievement of which requires rational public behavior that can only be obtained through full and accurate communication about risks and benefits. This paper describes how irrational behavior that threatens the effectiveness of vaccination programs--both in crisis and non-crisis situations--can be tied to public perceptions created by media portrayals of health risks. I concentrate on childhood vaccination as an exemplar of 'non-crisis' preventive vaccination, and on the recent flu vaccine shortage as a 'crisis' situation. The paper concludes with an examination of the steps necessary to resolve these threats through better public communication.     

Introduction to the modelling of venereal disease

Summary The continually rising trend in the incidence of venereal diseases, especially gonorrhoea, in a large number of countries, both developed and developing is causing considerable public health concern. There is a disquieting volume of human suffering involved, as well as large economic losses in treatment and hospitalization. The present paper reviews the existing state of development in the mathematical modelling of the relevant disease dynamics. The criss-cross nature of the infections, which in heterosexual contacts switch between the male and female populations, together with the nonlinear form of the rate of spread normally occurring in infectious diseases, leads to special types of simultaneous nonlinear differential equations.The simplest deterministic models available entail threshold phenomena connecting the maintenance of endemic states to the contact-rates, the personto-person infection-rates, and the removal-rates. A few stochastic results are also available.Special attention is given to the aspects of nonhomogeneous mixing, analysis of contact-rates, infection without immunity, allowance for asymptomatic infection, the recognition of many different classes of infected individuals, and the problems of public health forecasting and control. In some cases transient solutions of the equations can be used to forecast future trends in disease incidence, depending on appropriate assumptions about alternative public health interventions.It is concluded that further mathematical work should be concentrated on relatively simple models comprising no more than three or four district epidemiological groups for each sex. There should be both (i) more intense mathematical investigations, and (ii) new attempts to assimilate the models directly to public health venereal disease control.     

Spatio-temporal variation in environmental features predicts the distribution and abundance of Ixodes scapularis

Many species have experienced dramatic changes in both geographic range and population sizes in recent history. Increases in the geographic range or population size of disease vectors have public health relevance as these increases often precipitate the emergence of infectious diseases in human populations. Accurately identifying environmental factors affecting the biogeographic patterns of vector species is a long-standing analytical challenge, stemming from a paucity of data capturing periods of rapid changes in vector demographics. We systematically investigated the occurrence and abundance of nymphal Ixodes scapularis ticks at 532 sampling locations throughout New York State (NY), USA, between 2008 and 2018, a time frame that encompasses the emergence of diseases vectored by these ticks. Analyses of these field-collected data demonstrated a range expansion into northern and western NY during the last decade. Nymphal abundances increased in newly colonised areas, while remaining stable in areas with long-standing populations over the last decade. These trends in the geographic range and abundance of nymphs correspond to both the geographic expansion of human Lyme disease cases and increases in incidence rates. Analytic models fitted to these data incorporating time, space, and environmental factors, accurately identified drivers of the observed changes in nymphal occurrence and abundance. These models accounted for the spatial and temporal variation in the occurrence and abundance of nymphs and can accurately predict nymphal population patterns in future years. Forecasting disease risk at fine spatial scales prior to the transmission season can influence both public health mitigation strategies and individual behaviours, potentially impacting tick-borne disease risk and subsequently human disease incidence.     

Prediction of flu epidemic activity with dynamical model based on weather forecast

The seasonality of respiratory diseases (common cold, influenza, etc.) is a well-known phenomenon studied from ancient times. The development of predictive models is still not only an actual unsolved problem of mathematical epidemiology but also is very important for safety of public health. Here we show that SIRS (Susceptible-Infected-Recovered-Susceptible) model accurately enough reproduces real curves of flu activity. It contains variable reaction rate, which is a function of mean daily temperature.The proposed alternation of variables represents SIRS equations as the second-order ODE with an outer excitation. It reveals an origin of such predictive efficiency and explains analytically the 1:1 dynamical resonance, which is known as a crucial property of epidemic behavior. Our work opens the perspectives for the development of instant short-time prediction of a normal level of flu activity based on the weather forecast, and allows to estimate a current epidemic level more precisely. The latter fact is based on the explicit difference between the expected weather-based activity and instant anomalies.     

Infant mortality and child health in Brazil

Child health is a central issue in the public policy agenda of developing countries. Several policies aimed at improving child health have been implemented over the years, with varying degrees of success. In Brazil, such policies have triggered a significant decline in infant mortality rates over the last 30 years. Despite this improvement, however, mortality rates are still high compared to international standards. Moreover, there is considerable imbalance across Brazilian municipalities suggesting that various policies should be adopted. We investigate the determinants of infant mortality at the municipal level and provide an analysis of the factors affecting child health at the individual level. To analyze the mortality rate, we estimate static and dynamic panel data models using four censuses covering the period from 1970 to 2000. The demand for child health, on the other hand, is addressed through a household decision model, estimated using anthropometric data from the 1996 Standard of Living Survey. The results obtained indicate that a rise in sanitation, education and per capita income contributed to the decline of infant mortality in Brazil, with stronger impacts in the long run than in the short run. The fixed effects associated with county characteristics explain the observed dispersion in child mortality rates. The results from the decision model are confirmed by the findings of the mortality model: education, sanitation and poverty are the most important causes of poor child health in Brazil.     

Free to die: Economic freedoms and influenza mortality

Seasonal influenzas are annually responsible for hundreds of thousands of deaths worldwide, often because of insufficient care, which may depend on orientations of economic policy. Yet, the empirical evidence on the relations existing between policies based on different degrees of economic liberalism and flu mortality is still scarce. This paper contributes to filling the gap by proposing an empirical investigation into the effects of various dimensions of liberalism, proxied by the different components of the Fraser Index of Economic Freedom, on deaths from seasonal influenzas in a sample of 38 OECD countries observed from 1970 to 2018. A dynamic panel System-GMM estimator is used to alleviate endogeneity concerns, while alternative models, specifications and subsamples check the robustness of findings. Findings show that: a) not every component of economic freedom has an effect on flu mortality; b) more economic freedom not always means less or more deaths from flu. In particular, stronger protection of property rights and smaller government consumption are associated with higher flu mortality, which is instead lower when people and capital are freer to move. Such results give rise to policy considerations and contribute to inform policymakers about actions that can limit the mortality of a globally widespread disease like flu.     

Integer‐valued functional data analysis for measles forecasting

Measles presents a unique and imminent challenge for epidemiologists and public health officials: the disease is highly contagious, yet vaccination rates are declining precipitously in many localities. Consequently, the risk of a measles outbreak continues to rise. To improve preparedness, we study historical measles data both prevaccine and postvaccine, and design new methodology to forecast measles counts with uncertainty quantification. We propose to model the disease counts as an integer‐valued functional time series: measles counts are a function of time‐of‐year and time‐ordered by year. The counts are modeled using a negative‐binomial distribution conditional on a real‐valued latent process, which accounts for the overdispersion observed in the data. The latent process is decomposed using an unknown basis expansion, which is learned from the data, with dynamic basis coefficients. The resulting framework provides enhanced capability to model complex seasonality, which varies dynamically from year‐to‐year, and offers improved multimonth‐ahead point forecasts and substantially tighter forecast intervals (with correct coverage) compared to existing forecasting models. Importantly, the fully Bayesian approach provides well‐calibrated and precise uncertainty quantification for epi‐relevant features, such as the future value and time of the peak measles count in a given year. An R package is available online.