Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam

A scoping review of foot‐and‐mouth disease risk, based on spatial and spatio‐temporal analysis of outbreaks in endemic settings

“…Conventional GLM or GLMM Poisson (Arjkumpa et al., 2020 ; Kerfua et al., 2018 ), negative binomial (Gunasekera et al., 2017 ), linear (Aman et al., 2020 ; Gallego et al., 2007 ; Jemberu et al., 2016 ; Perez et al., 2011 ; Woldemariyam et al., 2022 ) or logistic regression (Jemberu et al., 2016 ) models were used to explore or test hypothesis related to temporal trends. Other studies resorted to Bayesian approaches (Branscum et al., 2008 ; Choi et al., 2012 ; Gunasekera et al., 2022 ; Richards et al., 2014 ), additive models (Aman et al., 2020 ), spectral analysis (Perez et al., 2011 ), locally weighted regression (Sanchez‐Vazquez et al., 2019 ), normalized temporal trends (Madin, 2011 ), time series (Gallego et al., 2007 ), regression tree models (Souley Kouato et al., 2018 ) and models fitted to inverted correlograms (Gilbert et al., 2005 ) to analyse temporal data. Moreover, 13 studies formally analysed FMD seasonality through the calculation of seasonal indexes (Abdrakhmanov et al., 2018 ; Gallego et al., 2007 ; Perez et al., 2011 ), seasonal decomposition (Madin, 2011 ; Woldemariyam et al., 2022 ), randomness tests (Aman et al., 2020 ; Gallego et al., 2007 ; Jemberu et al., 2016 ) or by fitting frequentist or Bayesian models to temporal data (Choi et al., 2012 ; Guerrini et al., 2019 ; Jafarzadeh et al., 2014 ; Kerfua et al., 2018 ; Rahman et al., 2020 ).…”

“…Two of these studies also addressed factors connected to disease notification (Chhetri et al., 2010 ; Jafarzadeh et al., 2014 ). Four studies focused only on forecasting FMD outbreaks using epidemiological factors as co‐variates to predict areal FMDv suitability, estimate spatial risk (number of outbreaks) or to detect spatial abnormalities in FMD passive surveillance data (Haoran et al., 2021 ; Perez et al., 2006 ; Richards et al., 2014 ; Sangrat et al., 2020 ).…”

“…Conventional GLM and GLMM models were commonly reported, including Poisson (Guerrini et al., 2019 ; Kerfua et al., 2018 ), logistic (Chimera et al., 2022 ; Gilbert et al., 2005 ; Jemberu et al., 2016 ; Sansamur et al., 2020 ), zero‐inflated binomial (Jafarzadeh et al., 2014 ), negative binomial (Gunasekera et al., 2017 ) and geographically weighted regressions (Rahman et al., 2020 ). Six models implemented a Bayesian framework to model the spatial or spatial‐temporal distribution of FMD (Allepuz et al., 2015 ; Branscum et al., 2008 ; Chhetri et al., 2010 ; Choi et al., 2012 ; Gunasekera et al., 2022 ; Richards et al., 2014 ). Other spatial analysis included the use of co‐kriging method to assess the probability of FMD incidence (Perez et al., 2006 ); Maximum Entropy Ecological Niche (MaxEnt) modelling to detect suitable areas for FMDV serotypes (Gao & Ma, 2021 ); GIS‐based multi‐criterion decision analysis to predict suitability for FMD occurrence (Haoran et al., 2021 ; Sangrat et al., 2020 ); point process model to explain outbreak intensity (Hamoonga et al., 2014 ) and regression tree analysis to identify predictors and interactions that influence FMD occurrence (Souley Kouato et al., 2018 ).…”

“…Research conducted in Low and Low/Middle Income Countries(LMICs) shows that decreased geographical accessibility can either result in a higher infectious disease burden or lower disease reporting, which in turn reduces the understanding of true disease patterns (Hierink et al., 2021 ). The link between spatial accessibility and reporting seems plausible in the context of FMD, especially in remote areas (Limon et al., 2020 ; MacPhillamy, Olmo, et al., 2022 ; MacPhillamy, Young, et al., 2022 ); therefore, strategies to discern or quantify true disease absence from lack of reporting should be implemented (Madin, 2011 ; Richards et al., 2014 ).…”

The Critical Role Of VP1 In Forming The Necessary Cavities For Receptor-mediated Entry Of FMDV To The Host Cell

“…FMD is an extremely infectious disease of livestock 4 5 6 , while hand-foot-and-mouth disease (HFMD) virus, another subfamily in the family Picornaviridae, has shown several outbreaks of the disease in humans 7 8 9 10 11 . FMD is mostly spread by direct and indirect animal contacts in specific areas while the movement of infected animals across international borders might be a major reason for the spatial epidemiology of FMD 12 .…”