Dengue and chikungunya: modelling the expansion of mosquito-borne viruses into naïve populations

Wearing, Helen J.; Michael, Robert; Christofferson, Rebecca C.

doi:10.1017/s0031182016000421

Cited by 12 publications

(26 citation statements)

References 147 publications

(197 reference statements)

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“…To our surprise, most of the empirical and modeling studies of DENV are focused on the urban cycle of transmission (Wearing et al 2016). Although this is expected given the economic and human health impacts of DENV in urban areas, we suggest that current model projections may fail in the long term if they disregard the role that wild reservoirs (and their phylogenetic, environmental and geographical limiting factors) may play in the ecological transmission of DENV.…”

Section: Discussionmentioning

confidence: 85%

“…Aedes albopcitus has higher tolerance to lower temperatures compared to Ae. aegypti, and also inhabits peri-urban and sub-urban areas feeding in a larger array of vertebrate hosts, making it ideal as a bridge vector across the human-domestic-wildlife interface (Wearing et al 2016).…”

Section: Discussionmentioning

confidence: 99%

“…into places with immunologically naïve populations poses a major risk (Wearing et al 2016; and Hay 2020), particularly because researchers do not know ahead of time what species will enter the sylvatic cycle of the disease (e.g., Sotomayor-Bonilla et al 2014). Thus, we need to develop more effective and preventive alternatives to reduce the risk of potential hazards becoming a real problem, and we also need to take the perspective of wild organisms and how they are affected by human activities (i.e., OneHealth or EcoHealth; Aguirre et al 2012; Ostfeld and Keesing 2017).…”

Section: Introductionmentioning

confidence: 99%

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American mammals susceptibility to dengue according to geographical, environmental and phylogenetic distances

Robles-Fernández

Santiago‐Alarcon

Lira‐Noriega

2020

Preprint

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Many human emergent and re-emergent diseases have a sylvatic cycle. Yet, little effort has been put into discovering and modeling the wild mammal reservoirs of dengue (DENV), particularly in the Americas. Here, we show a species-level susceptibility prediction to dengue of wild mammals in the Americas as a function of the three most important biodiversity dimensions (ecological, geographical, and phylogenetic spaces), using machine learning protocols. Model predictions showed that different species of bats would be highly susceptible to DENV infections, where susceptibility mostly depended on phylogenetic relationships among hosts and their environmental requirement. Mammal species predicted as highly susceptible coincide with sets of species that have been reported infected in field studies, but it also suggests other species that have not been previously considered or that have been captured in low numbers. Also, the environment (i.e., the distance between the species’ optima in bioclimatic dimensions) in combination with geographic and phylogenetic distance is highly relevant in predicting susceptibility to DENV in wild mammals. Our results agree with previous modeling efforts indicating that temperature is an important factor determining DENV transmission, and provide novel insights regarding other relevant factors and the importance of considering wild reservoirs. This modeling framework will aid in the identification of potential DENV reservoirs for future surveillance efforts.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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American mammals susceptibility to dengue according to geographical, environmental and phylogenetic distances

Robles-Fernández

Santiago‐Alarcon

Lira‐Noriega

2020

Preprint

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“…The Chikungunya virus infects humans through mosquitoes as the disease vector. The model adopted here is a compartmental model known as SEIR (Susceptible, Exposed, Infected, and Recovered) [8,10,14]. The approaches using this class of models have been successful in modeling epidemic related to human vector dynamics [11,15].…”

Section: Methodsmentioning

confidence: 99%

Modeling Chikungunya control strategies and Mayaro potential outbreak in the city of Rio de Janeiro

et al. 2020

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“…The Chikungunya virus infects humans 35 through mosquitoes as the disease vector. The model adopted here is a compartmental 36 model known as SEIR (Susceptible, Exposed, Infected, and Recovered) [8,10,14]. The 37 approaches using this class of models have been successful in modeling epidemic related 38 to human vector dynamics [11,15].…”

mentioning

confidence: 99%

Modeling Mayaro and Chikungunya Control Strategies in Rio de Janeiro Outbreaks

Dodero-Rojas

Ferreira

Leite

et al. 2019

Preprint

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Mosquito-borne diseases have become a significant health issue in many regions around the world. For tropical countries, diseases such as Dengue, Zika, and Chikungunya, became epidemic in the last decades. Health surveillance reports during this period were crucial in providing scientific-based information to guide decision making and resources allocation to control outbreaks. In this work, we perform data analysis of last Chikungunya epidemics in the city of Rio de Janeiro by applying a compartmental mathematical model. We estimate the "basic reproduction number" for those outbreaks and predict the potential epidemic outbreak of Mayaro virus. We also simulated several scenarios with different public interventions to decrease the number of infected people.Such scenarios should provide insights about possible strategies to control future outbreaks. September 5, 2019 2/19 In the last decades, Mosquito-borne diseases have become a significant health issue in 2 many regions around the world. Projections indicate that around 2050, half of the 3 population will be at risk of some arbovirus infection [1]. These arboviruses, which 4 include diseases such as Dengue, Zika, and Chikungunya, are epidemic in most of the 5 tropical countries. Besides temperature and humidity, human migrations and sanitation 6 also contribute to the epidemic conditions in these places [2, 3]. For example, around 7 300.000 people were infected by Dengue, Zika, or Chikungunya by the end of the 11 th 8 week of 2019 in Brazil. This number represents almost three times the reported cases in 9 2018 for the same period [4]. These surveillance reports over time are essential in 10 providing scientific-based information to guide decision making, resources allocation, 11 and interventions [5]. The usage of mathematical models has demonstrated to be a 12 powerful tool in contributing to these data analysis [6-8]. One of the most significant 13 parameters extracted from these analyses is the basic reproduction number R o . R o is 14 defined as the number of secondary infections derived from one single infectious subject 15 and is widely used as an epidemiologic metric employed to describe the transmissibility 16 of infectious agents [9]. 17 Here we apply a compartmental mathematical model to investigate the dynamics of 18 Chikungunya outbreaks in the city of Rio de Janeiro in Brazil. The model consists of 19 ordinary differential equations that describe the transmission and the transition of the 20 diseases in humans and vectors [10, 11]. The model's parameters were extracted from 21 the literature or obtained from the best fit from the data of Rio de Janeiro surveillance 22 report for the years of 2016, 2018 and 2019 [12]. Based on these parameters, we estimate 23 the basic reproduction number R o for Chikungunya outbreaks in those years. We also 24 simulate a scenario predicting if the Mayaro virus could be a potential epidemic disease 25 in Rio de Janeiro. Modifications in the standard model equations were implemented to 26 introduce different possible ...

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Dengue and chikungunya: modelling the expansion of mosquito-borne viruses into naïve populations

Cited by 12 publications

References 147 publications

American mammals susceptibility to dengue according to geographical, environmental and phylogenetic distances

American mammals susceptibility to dengue according to geographical, environmental and phylogenetic distances

Modeling Chikungunya control strategies and Mayaro potential outbreak in the city of Rio de Janeiro

Modeling Mayaro and Chikungunya Control Strategies in Rio de Janeiro Outbreaks

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