A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data

Pergantas, Panagiotis; Tsatsaris, Andreas; Malesios, Chrisovalantis; Kriparakou, Georgia; Demiris, Nikolaos; Tselentis, Yiannis

doi:10.1371/journal.pone.0178836

Cited by 17 publications

(24 citation statements)

References 29 publications

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“…The most common approach to the assessment of possible malaria re-introduction is the analysis of distribution of vectors and changes in their bionomics [68,69]. Climatic and social factors are often added [70,71], but they are first considered from the point of view of their possible effect on vectors. However, in recent post-eradication outbreaks in Europe, Russia included, changes in vector factors have never been the trigger for the resumption of transmission, and re-appearance of autochthonous malaria was due to either intensification of its importation, or meteorological factors favouring the parasite maturation, or a combination thereof [72].…”

Section: Discussionmentioning

confidence: 99%

Re-introduction of vivax malaria in a temperate area (Moscow region, Russia): a geographic investigation

Mironova

Shartova

Beljaev

et al. 2020

Malar J

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Background: Between 1999 and 2008 Russia experienced a flare-up of transmission of vivax malaria following its massive importation with more than 500 autochthonous cases in European Russia, the Moscow region being the most affected. The outbreak waned soon after a decrease in importation in mid-2000s and strengthening the control measures. Compared with other post-eradication epidemics in Europe this one was unprecedented by its extension and duration. Methods: The aim of this study is to identify geographical determinants of transmission. The degree of favourability of climate for vivax malaria was assessed by measuring the sum of effective temperatures and duration of season of effective infectivity using data from 22 weather stations. For geospatial analysis, the locations of each of 405 autochthonous cases detected in Moscow region have been ascertained. A MaxEnt method was used for modelling the territorial differentiation of Moscow region according to the suitability of infection re-emergence based on the statistically valid relationships between the distribution of autochthonous cases and environmental and climatic factors. Results: In 1999-2004, in the beginning of the outbreak, meteorological conditions were extremely favourable for malaria in 1999, 2001 and 2002, especially within the borders of the city of Moscow and its immediate surroundings. The greatest number of cases occurred at the northwestern periphery of the city and in the adjoining rural areas. A significant role was played by rural construction activities attracting migrant labour, vegetation density and landscape division. A cutoff altitude of 200 m was observed, though the factor of altitude did not play a significant role at lower altitudes. Most likely, the urban heat island additionally amplified malaria re-introduction. Conclusion: The malariogenic potential in relation to vivax malaria was high in Moscow region, albeit heterogeneous. It is in Moscow that the most favourable conditions exist for vivax malaria re-introduction in the case of a renewed importation. This recent event of large-scale re-introduction of vivax malaria in a temperate area can serve as a case study for further research.

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Section: Discussionmentioning

confidence: 99%

Re-introduction of vivax malaria in a temperate area (Moscow region, Russia): a geographic investigation

Mironova

Shartova

Beljaev

et al. 2020

Malar J

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“…In (1) and 2 [20], which are currently represented by monthly means of temperature [11]. An analytical description of parameters and relevant distributions utilized for the calculations of risk measures is included in Table A3 in Appendix A.…”

Section: The Spatial Predictive Modelmentioning

confidence: 99%

“…In 3  , is then multiplied by a pre-specified incidence rate. A deterministic sensitivity analysis was conducted [11] and the overall qualitative picture remained unchanged although the quantitative scale varied somewhat.…”

Section: The Spatial Predictive Modelmentioning

confidence: 99%

“…Τhis activity allows mosquitoes to transmit several pathogens and parasites, becoming extremely important insects for human health [7]. Taking into account the invasive success of several mosquito species due to worldwide human mobility and trade [10], the development of early warning and control methods seems imperative [11]. Tropical VBDs such as malaria have attracted much attention due to their impact on human health [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases

Pergantas¹,

Papanikolaou

Malesios

et al. 2021

IJERPH

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The emergence and spread of vector-borne diseases (VBDs) is a function of biotic, abiotic and socio-economic drivers of disease while their economic and societal burden depends upon a number of time-varying factors. This work is concerned with the development of an early warning system that can act as a predictive tool for public health preparedness and response. We employ a host-vector model that combines entomological (mosquito data), social (immigration rate, demographic data), environmental (temperature) and geographical data (risk areas). The output consists of appropriate maps depicting suitable risk measures such as the basic reproduction number, R0, and the probability of getting infected by the disease. These tools consist of the backbone of a semi-automatic early warning system tool which can potentially aid the monitoring and control of VBDs in different settings. In addition, it can be used for optimizing the cost-effectiveness of distinct control measures and the integration of open geospatial and climatological data. The R code used to generate the risk indicators and the corresponding spatial maps along with the data is made available.

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“…For example, some process-based models consider the impact of temperature [141,142,143,144,145,146,147,148] or rainfall [147,149,150,151] on mosquito lifespan and development rates without explicitly considering the nonlinear response of mosquito habitat to weather (i.e., formation and persistence of standing water in the landscape). Recent modeling studies (e.g., malaria [24,152,153] and Rift Valley fever [154]) have begun to consider the linkage to hydrology and climate. But they are either limited to small scales (e.g., watersheds or small regions) [153,154] or are based on a simple calculation of water balance without considering real land surface characteristics [152].…”

Section: Epidemiological Modeling Of Vector-borne Infectious Diseasesmentioning

confidence: 99%

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Bartlow

Manore

et al. 2019

Veterinary Sciences

126

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Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data

Cited by 17 publications

References 29 publications

Re-introduction of vivax malaria in a temperate area (Moscow region, Russia): a geographic investigation

Re-introduction of vivax malaria in a temperate area (Moscow region, Russia): a geographic investigation

Towards a Semi-Automatic Early Warning System for Vector-Borne Diseases

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

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