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      Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data

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          Abstract

          Introduction

          Rabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques.

          Methods

          Monthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset.

          Results

          The findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18–30). In contrast, SARIMA projections averaged 277 cases annually (range: 208–214).

          Discussion

          This research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities.

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          Most cited references49

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          Estimating the Global Burden of Endemic Canine Rabies

          Background Rabies is a notoriously underreported and neglected disease of low-income countries. This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations, globally and on a country-by-country basis, allowing an objective assessment of how much this preventable disease costs endemic countries. Methodology/Principal Findings We established relationships between rabies mortality and rabies prevention and control measures, which we incorporated into a model framework. We used data derived from extensive literature searches and questionnaires on disease incidence, control interventions and preventative measures within this framework to estimate the disease burden. The burden of rabies impacts on public health sector budgets, local communities and livestock economies, with the highest risk of rabies in the poorest regions of the world. This study estimates that globally canine rabies causes approximately 59,000 (95% Confidence Intervals: 25-159,000) human deaths, over 3.7 million (95% CIs: 1.6-10.4 million) disability-adjusted life years (DALYs) and 8.6 billion USD (95% CIs: 2.9-21.5 billion) economic losses annually. The largest component of the economic burden is due to premature death (55%), followed by direct costs of post-exposure prophylaxis (PEP, 20%) and lost income whilst seeking PEP (15.5%), with only limited costs to the veterinary sector due to dog vaccination (1.5%), and additional costs to communities from livestock losses (6%). Conclusions/Significance This study demonstrates that investment in dog vaccination, the single most effective way of reducing the disease burden, has been inadequate and that the availability and affordability of PEP needs improving. Collaborative investments by medical and veterinary sectors could dramatically reduce the current large, and unnecessary, burden of rabies on affected communities. Improved surveillance is needed to reduce uncertainty in burden estimates and to monitor the impacts of control efforts.
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            Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing

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              The theta model: a decomposition approach to forecasting

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                Author and article information

                Contributors
                URI : http://loop.frontiersin.org/people/378982/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
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                URI : http://loop.frontiersin.org/people/1949781/overviewRole: Role: Role:
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                URI : http://loop.frontiersin.org/people/753175/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                29 November 2023
                2023
                : 10
                : 1294049
                Affiliations
                [1] 1Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University , Chiang Mai, Thailand
                [2] 2Veterinary Public Health and Food Safety Centre for Asia Pacific, Faculty of Veterinary Medicine, Chiang Mai University , Chiang Mai, Thailand
                [3] 3Department of Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University , Chiang Mai, Thailand
                [4] 4Department of Livestock Development , Bangkok, Thailand
                [5] 5Companion Disease Control Division, Bureau of Disease Control and Veterinary Services, Department of Livestock Development , Bangkok, Thailand
                [6] 6College of Veterinary Science and Medicine, Central Luzon State University, Science City of Muñoz , Nueva Ecija, Philippines
                [7] 7The 4th Regional Livestock Office, Department of Livestock Development , Khon Kaen, Thailand
                Author notes

                Edited by: Javier Caballero Gómez, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Spain

                Reviewed by: A. K. M. Anisur Rahman, Bangladesh Agricultural University, Bangladesh; Rouzbeh Bashar, Pasteur Institute of Iran, Iran

                *Correspondence: Veerasak Punyapornwithaya veerasak.p@ 123456cmu.ac.th
                Article
                10.3389/fvets.2023.1294049
                10716232
                38094496
                551ae198-2665-4d1a-9dea-83f56a5190a5
                Copyright © 2023 Punyapornwithaya, Thanapongtharm, Jainonthee, Chinsorn, Sagarasaeranee, Salvador and Arjkumpa.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 September 2023
                : 08 November 2023
                Page count
                Figures: 6, Tables: 1, Equations: 4, References: 53, Pages: 10, Words: 6872
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors are grateful for research funding from Chiang Mai University (Grant: R66IN00356).
                Categories
                Veterinary Science
                Original Research
                Custom metadata
                Veterinary Epidemiology and Economics

                rabies,confirmed cases,time series model,forecasting,thailand

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