Willingness to pay for an early warning system for infectious diseases.
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ABSTRACT:
SUBMITTER: Himmler S
PROVIDER: S-EPMC7364296 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic highlights the need for effective infectious disease outbreak prevention. This could entail installing an integrated, international early warning system, aiming to contain and mitigate infectious diseases outbreaks. The amount of resources governments should spend on such preventive measures can be informed by the value citizens attach to such a system. This was already recognized in 2018, when a contingent valuation willingness to pay (WTP) experiment was fielded, eliciting the WTP for such a system in six European countries. We replicated that experiment in the spring of 2020 to test whether and how WTP had changed during an actual pandemic (COVID-19), taking into account differences in infection rates and stringency of measures by government between countries. Overall, we found significant increases in WTP between the two time points, with mean WTP for an early warning system increasing by about 50% (median 30%), from around €20 to €30 per month. However, there were marked differences between countries and subpopulations, and changes were only partially explained by COVID-19 burden. We discuss possible explanations for and implication of our findings.
| S-EPMC8294297 | biostudies-literature
Project description:In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.
| S-EPMC7062078 | biostudies-literature
Project description:ObjectiveInfectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data.MethodsBased on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error.ResultsThe proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively.ConclusionThe MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.
| S-EPMC9410942 | biostudies-literature
Project description:Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.
| S-EPMC7680955 | biostudies-literature
Project description:Afghanistan's public health system was neglected during decades of military and civil conflict, and trends in infectious disease occurrence remain poorly characterized. This study examines cyclical and long-term trends of six vaccine-preventable diseases: pneumonia, diarrhea, meningitis, typhoid, measles, and acute viral hepatitis.Using weekly data collected between 2009 and 2015 through Afghanistan's Disease Early Warning System, we calculated monthly case counts, and fit a Poisson regression with a Fourier transformation for seasonal cycles and dummy variables for year.We found the greatest incidence of diarrhea and typhoid in the summer, pneumonia in the winter, and measles in the late spring. Meningitis and acute viral hepatitis did not demonstrate substantial seasonality. Rates of pneumonia and diarrhea were constant across years whereas rates of meningitis, typhoid, and acute viral hepatitis decreased. Measles incidence increased in 2015.Communicable disease reporting systems can guide public health operations-such as the implementation of new vaccines, and permit evaluation of health interventions. For example, measles supplementary immunization activities in Afghanistan have not slowed long-term transmission of the disease, but decreases in typhoid fever and acute viral hepatitis are probably tied to improvements in sanitation in the country.
| S-EPMC5453561 | biostudies-other
Project description:Breweries across the country are investing in energy efficient and low-carbon brewing practices. Drawing insights from the sustainable consumption and ecological economics literature, this analysis evaluates whether consumers are willing to pay more for sustainable beer and what predicts the value of the premium. Based on a survey of beer consumers from across the U.S. that contained one of two willingness-to-pay exercises, we evaluate what respondent attributes are associated with a higher willingness-to-pay for sustainably brewed beer. We find that the majority of beer consumers are willing to pay more for sustainable beer. Consumers who are prepared to pay a premium tend to already pay more per unit of beer, are more aware of their purchasing behavior and the manner in which their consumption patterns may affect the environment, and pursue lifestyles based on professional advancement, helping the environment, and helping other causes.
| S-EPMC6173403 | biostudies-literature
Project description:Surveillance of infectious diseases in livestock is traditionally carried out at the farms, which are the typical units of epidemiological investigations and interventions. In Central and Western Europe, high-quality, long-term time series of animal transports have become available and this opens the possibility to new approaches like sentinel surveillance. By comparing a sentinel surveillance scheme based on markets to one based on farms, the primary aim of this paper is to identify the smallest set of sentinel holdings that would reliably and timely detect emergent disease outbreaks in Swiss cattle. Using a data-driven approach, we simulate the spread of infectious diseases according to the reported or available daily cattle transport data in Switzerland over a four year period. Investigating the efficiency of surveillance at either market or farm level, we find that the most efficient early warning surveillance system [the smallest set of sentinels that timely and reliably detect outbreaks (small outbreaks at detection, short detection delays)] would be based on the former, rather than the latter. We show that a detection probability of 86% can be achieved by monitoring all 137 markets in the network. Additional 250 farm sentinels-selected according to their risk-need to be placed under surveillance so that the probability of first hitting one of these farm sentinels is at least as high as the probability of first hitting a market. Combining all markets and 1000 farms with highest risk of infection, these two levels together will lead to a detection probability of 99%. We conclude that the design of animal surveillance systems greatly benefits from the use of the existing abundant and detailed animal transport data especially in the case of highly dynamic cattle transport networks. Sentinel surveillance approaches can be tailored to complement existing farm risk-based and syndromic surveillance approaches.
| S-EPMC7787438 | biostudies-literature
Project description:Purpose of reviewIn 2013, NIH NIDA released a special funding opportunity to update and expand their longstanding Community Epidemiology Workgroup (CEWG) by creating a coordinating center for a new type of monitoring system that became the National Drug Early Warning System (NDEWS). This article provides an overview of four primary NDEWS components: core staff; an approach to detecting and monitoring emerging drug trends including community-based epidemiologists and national databases; small rapid follow-up studies; and innovative multimedia approaches for disseminating information.Recent findingsThe cornerstones for detecting and monitoring are the 1700+ member NDEWS Network and a coalition of local epidemiologists. Follow-up studies are designed with local researchers, practitioners, and policymakers to focus on local trends. Among NDEWS accomplishments are developing protocols for expanded urinalyses in high-risk populations and conducting follow-up studies with local collaborators in New Hampshire, New York, Ohio, Oregon, and a tribal nation in Minnesota.SummaryDuring its first 6 years, NDEWS has advanced its mission to develop multidisciplinary collaborations and innovative technologies for identifying, monitoring, and following up on emerging drug trends and has contributed to the translation of science into practice. VIDEO ABSTRACT: http://links.lww.com/YCO/A55.
| S-EPMC7386464 | biostudies-literature
Project description:BackgroundThe coronavirus disease (COVID-19) pandemic has considerably affected the lives of people worldwide, impacting their health and economic welfare, and changing the behavior of our society significantly. This situation may lead to a strong incentive for people to buy a vaccine. Therefore, a relevant study to assess individuals' choices and the value of change in welfare from a COVID-19 vaccine is essential.ObjectiveThis study aimed to estimate the willingness-to-pay (WTP) value for a vaccine for COVID-19. We also identify the variables that influence individual vaccination decisions, which could be used in the design of vaccination promotion strategies.MethodsWe use the contingent valuation method in its double-bounded dichotomous choice format. The estimation coefficients are calculated according to the maximum likelihood method under the assumption of a probit distribution. The sample consisted of 531 individuals, mainly from middle- and high-income socioeconomic groups from Chile between enrolled between 10 July and 10 August 2020.ResultsThe results show a high WTP for the COVID-19 vaccine, with a value up to US$232. Income and education levels and having family members with COVID-19 increased the likelihood of persons paying for a vaccine. There is also a greater fear as the pandemic progresses that people will get sick from COVID-19.ConclusionsThe high WTP value creates an opportunity for formulating public health policy. The results of this study suggest that governments can provide the vaccine free to low-income groups and allow those with higher incomes to acquire the vaccine through the private sector by paying. This will be useful especially for countries with economic difficulties.
| S-EPMC7899739 | biostudies-literature
Project description:Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R(2) for forecasts of "out-of-fit" data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.this study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of "out-of-fit" data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.
| S-EPMC2041810 | biostudies-literature