Abstract:Dengue is a vector-borne disease transmitted by the
Aedes
genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is… Show more
“…malaria and dengue) (electronic supplementary material, table S1). There were two studies published in 2020 that used a combination of methods: one compared a mechanistic and machine learning approach to predicting dengue transmission [ 19 ], another used both a machine learning and statistical approaches to explore the relationship between risk factors and dengue outbreaks [ 20 ]. Figure 2 Number of spatial modelling studies published per year by model type.…”
Section: Resultsmentioning
confidence: 99%
“…We identified two papers that constructed a matrix reflecting the movement of people between districts using public transportation data [ 19 , 237 ]. Both papers used this matrix, similar to the one shown in figure 3 d , to weight layers within a neural network model, allowing the algorithm to predict the number of dengue cases across the study area while accounting for connectivity arising from human mobility.…”
Section: Resultsmentioning
confidence: 99%
“…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 ].…”
Section: Resultsmentioning
confidence: 99%
“…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.…”
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an
Aedes
mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
“…malaria and dengue) (electronic supplementary material, table S1). There were two studies published in 2020 that used a combination of methods: one compared a mechanistic and machine learning approach to predicting dengue transmission [ 19 ], another used both a machine learning and statistical approaches to explore the relationship between risk factors and dengue outbreaks [ 20 ]. Figure 2 Number of spatial modelling studies published per year by model type.…”
Section: Resultsmentioning
confidence: 99%
“…We identified two papers that constructed a matrix reflecting the movement of people between districts using public transportation data [ 19 , 237 ]. Both papers used this matrix, similar to the one shown in figure 3 d , to weight layers within a neural network model, allowing the algorithm to predict the number of dengue cases across the study area while accounting for connectivity arising from human mobility.…”
Section: Resultsmentioning
confidence: 99%
“…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 ].…”
Section: Resultsmentioning
confidence: 99%
“…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.…”
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an
Aedes
mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
“…The optimized dynamical model is then integrated into the future to generate probabilistic forecasts. Similar model-data assimilation forecast frameworks have been successfully used for forecasting and inference of a variety of infectious diseases 3,[60][61][62][63][64][65] . Details about the system configuration can be found in Supplementary Note 2.…”
Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens – human metapneumovirus and seasonal coronavirus – from 35 US states and can be used to guide systemic allocation of surveillance efforts.
During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatiotemporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.
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