Multi-season analysis reveals the spatial structure of disease spread

Abstract

Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we develop a novel tensor-driven multi-compartment version of the classic Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. The model is used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV). We fit the data with correlations of R 2 = 0.70, and 0.52 for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27-30% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent seasons can be predicted using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

“…Examples of these matrices constructed using different assumptions and data are given in figure 3 . Matrices were constructed using human movement data from Twitter [ 32 , 251 , 256 ], air travel [ 239 , 249 , 250 ] or public transportation [ 19 ], using movement models that aimed to replicate human commuting behaviour [ 32 , 241 , 243 , 244 , 246 , 248 , 254 , 255 , 257 ], distance [ 242 ] or using a fixed value based on the type of neighbourhood [ 252 , 253 ]. Two studies estimated people's home and work addresses using mobile phone data and simulated movement between those [ 245 , 247 ], and two simulated the short flight distance of mosquitoes by allowing movement into neighbouring cells [ 240 , 245 ].…”

“…There were 21 studies (21/34, 61.8%) included in the review that used a movement matrix within a mechanistic model to account for spatial connectivity [19,32,[239][240][241][242][243][244][245][246][247][248][249][250][251][252][253][254][255][256][257]; all these studies assumed that connectivity arose from either host or vector movement. These models treated subgroups of the host and/or vector populations as nodes in a network with values of the matrix reflecting movement between those nodes.…”

Use of mathematical modelling to assess respiratory syncytial virus epidemiology and interventions: a literature review

“…Alternatively, some RSV DTMs implement more complicated ageing schemes to maintain realistic age structures (Acedo et al 2010a, b;Kinyanjui et al 2020;Yamin et al 2016). A few models provide additional demographic structure through organization of the simulation population by household (Brand et al 2020;Campbell et al 2020;Kombe et al 2019;Mahikul et al 2019), household and primary school (Poletti et al 2015), and geography (Seroussi et al 2020). A more detailed discussion of demographic structure is presented in Supplemental Materials 1: Appendix A.2.…”

“…These data mostly consist of RSV detected in inpatient settings only, i.e., hospitalizations, or in inpatient and outpatient settings. One model uses Google searches for the term "RSV" as a proxy for the number of RSV infections (Seroussi et al 2020). Data have been gathered for infants (< 1-year-olds) (Acedo et al 2010a, b;Campbell et al 2020;Corberán-Vallet and Santonja 2014;Jornet-Sanz et al 2017;White et al 2005), toddlers (< 2-yearolds) (Hogan et al 2016(Hogan et al , 2017Moore et al 2014;Paynter et al 2014;Ponciano and Capistrán 2011;Weber et al 2001;White et al 2007), young children (< 5-year-olds) (Aranda-Lozano et al 2013;Arenas et al 2009Arenas et al , 2010Hodgson et al 2020;Kinyanjui et al 2015Kinyanjui et al , 2020Pan-Ngum et al 2017;Poletti et al 2015;White et al 2007), children (Leecaster et al 2011;Hodgson et al 2020;Nugraha and Nuraini 2017;Ponciano and Capistrán 2011;Weber et al 2001;White et al 2005White et al , 2007, and the entire population (Arguedas et al 2019;Baker et al 2019;Brand et al 2020;Goldstein et al 2018;Hodgson et al 2020;Kombe et al 2019;Mahikul et al 2019;Pitzer et al 2015;Shaman 2016, 2018;…”

Stochastic probical strategies in a delay virus infection model to combat COVID-19

“…Mathematical modeling techniques are reliable mechanisms widely adopted by researchers [1] , [2] , [3] , [4] to develop frameworks in tackling environmental crises efficiently. Mathematical model structures and their analytical techniques have capitulated research interests of numerous biological scientists as they bestow them liberty to experiment without any direct species interaction yet furnishing exquisite results in compliance with experimental outcomes.…”