Vector-borne diseases: studies in human West Nile Virus and canine Lyme nephritis

Vector-borne diseases are a resurgent focus in public health. As concern about climate change mounts, the close relationship between these diseases and the environment has garnered growing attention. This dissertation examines the relationship between environment and vector-borne disease in both human and veterinary medical contexts and on both a local and national scale. The first study investigated using a novel Internet-based surveillance system for risk mapping of West Nile Virus (WNV) in the contiguous United States from 2007-2014, with meteorological, demographic, and land use variables as predictors. The study found that annual average temperature, minimum temperature, precipitation, and human population density were predictive of WNV reports, but that the novel surveillance data appeared to have systematic gaps that impair the utility of the model. However, the results may help to guide improvements in novel surveillance systems. The second study used the logistic regression model developed in the first study to predict the risk of WNV in the contiguous United States in 2050 and 2070 under four projected climate scenarios. The study found that Southern California is likely to remain the area of greatest risk under all scenarios and that risk would be expected to increase across much of the West under the scenario of uncontrolled carbon dioxide emissions. The results of this study may inform development of more sophisticated models and may help to direct public health resources to areas of greatest impact. The third study investigated the relationship between cases of canine Lyme nephritis and precipitation in the months prior to diagnosis. Precipitation three months prior to diagnosis was found to be associated with the development of Lyme nephritis (hazard ratio for 1 inch/month 1.125, 95% confidence interval 1.009 – 1.254). This finding may improve diagnostic accuracy for dogs with protein-losing nephropathies and may guide studies of additional risk factors

Understanding and predicting mosquito-borne disease under current and future scenarios of global change

There is a rapidly growing awareness of the influence of global change processes such as land-use, climate change and socioeconomic factors on the burden of mosquito-borne disease (MBD). Although individual effects of different processes on MBD risk have been studied widely, a holistic approach that considers the combined influence of different global change processes has rarely been implemented. Here, I evaluate the effects of different global change processes on MBD risk, both generally, and in a series of modelling studies using the understudied MBD, Japanese encephalitis (JE) as a case study. I integrate different data types and approaches from ecology and epidemiology, with the aim of informing public health decision-makers in the era of accelerating global change. Firstly, I synthesise current knowledge on relative and interacting effects of global change processes on MBD risk and examine how these factors have been incorporated into existing analyses, highlighting how future research could be improved. Secondly, I compile a vector surveillance database for the predominant vector of JE (Culex tritaeniorhynchus). I use a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance over large spatial scales, that has the potential to be used to provide guidance for the targeting of suitable interventions. I use this information in an epidemiological study of JE case surveillance data and show that human JE incidence is associated with climate, land-use and socioeconomic factors, and these factors can be used to predict JE outbreaks in north-eastern India. Thirdly, I examine possible trends in JE epidemiology by projecting into the future under various scenarios of global change to show divergence in JE risk and burden under different socioeconomic and environmental policy scenarios. Finally, I integrate the implications of these results into our understanding of the effects of global change processes on MBD, the epidemiology and control of JE, and a holistic approach to the understanding and prediction of MBD risk

Ears of the Armadillo: Global Health Research and Neglected Diseases in Texas

Neglected tropical diseases (NTDs) have\ud been recently identified as significant public\ud health problems in Texas and elsewhere in\ud the American South. A one-day forum on the\ud landscape of research and development and\ud the hidden burden of NTDs in Texas\ud explored the next steps to coordinate advocacy,\ud public health, and research into a\ud cogent health policy framework for the\ud American NTDs. It also highlighted how\ud U.S.-funded global health research can serve\ud to combat these health disparities in the\ud United States, in addition to benefiting\ud communities abroad

Modeling of the spatiotemporal distribution patterns and transmission dynamics of dengue, for an early warning surveillance system

As doenças emergentes transmitidas por vetores representam um desafio significativo para a saúde pública global. Nos últimos tempos, os surtos de doenças como a dengue e a febre de chikungunya, aumentaram em frequência. Tal é facilitado pela globalização, pelo aumento do comércio e das viagens, e pela dispersão para novas áreas dos seus vetores invasores. Na Europa, este facto é exemplificado pela recente introdução e estabelecimento de espécies de mosquitos do género Aedes com a subsequente ocorrência de surtos de doenças como a dengue. Com a crescente disseminação da dengue em todo o mundo, a região europeia também tem vindo a registar um aumento de casos - a maioria destes relacionados com viagens. Da mesma forma, tem havido um aumento de eventos esporádicos de transmissão autóctone de dengue em áreas onde ocorre o vetor sob condições ambientais favoráveis. Assim, atualmente, a Europa enfrenta o desafio de avaliar o risco de importação de casos virémicos de dengue e a probabilidade de ocorrência de transmissão local deste vírus. Esta tese visa contribuir para a compreensão dos fatores relacionados com a importação do vírus da dengue na Europa e a sua transmissão neste território, nomeadamente na ilha da Madeira. Para tal foi implementado uma estrutura integrada de modelos computacionais da importação e transmissão da doença. A estrutura combina três submodelos: (i) um modelo explicativo de importação da doença assente em teoria de redes (ii) um modelo preditivo de aprendizagem automática e, (iii) um modelo compartimental de transmissão vetor-hospedeiro. Os modelos de teoria de redes e de aprendizagem automática foram parametrizados com recurso a dados históricos referentes a estimativas de casos importados de dengue em 21 países na Europa e índices que caracterizam parâmetros com relevância na importação da dengue: (i) tráfego de passageiros aéreos, (ii) atividade e sazonalidade da dengue, (iii) taxa de incidência, (iv) proximidade geográfica, (v) vulnerabilidade à epidemia, e, (vi) contexto económico do país de origem. O modelo compartimental de transmissão foi calibrado com parâmetros empíricos referentes ao ciclo de vida do mosquito, à transmissão viral e à variação anual de temperatura do Funchal, na ilha da Madeira. Os resultados dos modelos de teoria de redes e aprendizagem automática demonstram um maior risco de importação de casos virémicos de países com elevado tráfego de passageiros, elevadas taxas de incidência, situação económica débil e com maior proximidade geográfica em relação ao país de destino. O modelo de aprendizagem automática alcançou elevada performance preditiva, com uma pontuação AUC de 0,94. O modelo compartimental de transmissão demonstra a existência de um potencial de transmissão da dengue no Funchal nos períodos de verão e outono, com a data de chegada da pessoa infeciosa a afetar significativamente a distribuição no tempo e tamanho do pico da epidemia. Da mesma forma, a variação sazonal da temperatura afeta dramaticamente a dinâmica da epidemia, em que temperaturas iniciais mais quentes levam a surtos de maiores proporções, com o pico de casos a ocorrer mais cedo. A estrutura de modelação descrita nesta tese tem o potencial de servir como uma ferramenta integrada de vigilância de alerta precoce para a ocorrência de surtos de dengue na Europa. Este trabalho fornece orientação prática para auxiliar as autoridades de saúde pública na prevenção de surtos de dengue e na redução do risco de transmissão local, em áreas onde ocorrem os vetores. Essa estrutura, com os devidos reajustamentos, pode ser aplicada a outras doenças transmitidas por Aedes, como chikungunya e febre amarela.Emerging vector-borne diseases pose a significant global public health challenge. In recent times, outbreaks of diseases, such as dengue and chikungunya fever, have increased in frequency. This is facilitated by globalization, increase in trade and travel, and the spread of invasive vectors into new areas. In Europe, this is exemplified by the recent introduction and establishment of Aedes mosquito species and subsequent outbreaks of diseases like dengue. With the increasing spread of dengue worldwide, the European region has also experienced increase in reported cases - majority being travel related. Likewise, there has been an increase in sporadic events of autochthonous dengue transmission, in areas with established vector presence and favourable environmental conditions. Europe is currently faced with the challenge of assessing its importation risk of viraemic cases of dengue, and the probability of local transmission. This thesis aims to study the dynamics of viraemic cases importation and virus transmission of dengue fever in Europe, namely in Madeira Island. This is achieved by establishing an importation and transmission modelling framework. The framework combines three sub-models: (i) a network connectivity importation model (ii) a machine learning predictive model and, (iii) a compartmental vector-host transmission model. The network connectivity and machine learning model were both parameterized using a historical dengue importation data for 21 countries in Europe, and indices that characterize important parameters for dengue importation: (i) the air passenger traffic, (ii) dengue activity and seasonality, (iii) incidence rate, (iv) geographical proximity, (v) epidemic vulnerability, and (vi) wealth of a source country. The transmission model was calibrated using empirical parameters for the mosquito life history traits, viral transmission, and temperature seasonality of Funchal, Madeira Island. The results of the network connectivity and machine learning models demonstrate a higher importation risk of a viraemic case from source countries with high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country. The machine learning model achieved high predictive accuracy with an AUC score of 0.94. The transmission model demonstrates the potential for summer and autumn season transmission of dengue in Funchal, with the arrival date of the infectious person significantly affecting the distribution of the timing and peak size of the epidemic. Likewise, seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with peaks occurring more rapidly. The modelling framework described in this thesis has the potential to serve as an integrated early warning surveillance tool for dengue in Europe. This work provides practical guidance to assist public health officials in preventing outbreaks of dengue and reducing the risk of local transmission in areas with vectors presence. This framework could be applied to other Aedes-borne diseases such as chikungunya and yellow fever

Climate and livestock disease: assessing the vulnerability of agricultural systems to livestock pests under climate change scenarios

Livestock as a sector is extremely important to the global economy and to rural livelihoods. As of 2013, there was an estimated 38 billion livestock in the world, or five animals for every person. Most (81%) were in developing countries. Around one billion smallholder farmers keep livestock, many of them women. The burden of animal disease in developing countries is high: livestock disease probably kills 20% of ruminants and more than 50% of poultry each year causing a loss of approximately USD 300 billion per year. Climate change can exacerbate disease in livestock, and some diseases are especially sensitive to climate change. Among 65 animal diseases identified as most important to poor livestock keepers, 58% are climate sensitive. Climate change may also have indirect effects on animal disease, and these may be greater than the direct effects

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models

Urban Ecology and the Effectiveness of Aedes Control

Past initiatives to control Aedes mosquitoes were successful, in part because they implemented draconian top-down control programs. To achieve similar results now, explicit recognition of the complexity in urban ecologies in terms of land ownership, law enforcement and accessibility for control interventions are required. By combining these attributes, four classes of spaces, along with corresponding control strategies, are suggested to better target Aedes species population control efforts. On one end of the spectrum there are accessible and accountable spaces (e.g. backyards and closely managed public facilities), where interventions can rely predominantly on bottom-up strategies with the local population playing the principle role in the implementation of actions, but with government coordination. On the other end of the spectrum are inaccessible and unaccountable spaces, which require top-down and extensive approaches. By identifying these and the intermediate classes of space, government and private resources can be allocated in a more efficient customized manner. Based on this new framework, a set of actions is proposed that might be implemented in dengue and other Aedes-borne crises. The framework considers existing limitations and opportunities associated with modern societies–which are fundamentally different from those associated with the successful control of Aedes species in the past

Dengue in Finnish international travelers, 2016–2019:a retrospective analysis of places of exposure and the factors associated with the infection

Abstract. As an emerging infectious disease dengue is putting a constantly growing number of international tourists at risk of the infection. To have a more complete picture of the phenomena among the Finnish travelers, the backgrounds of infections were retrospectively examined to find out the place of exposure, type of traveler and the trip, risk perceptions and protective measures taken. The study period was from January 2016 to May 2019 and reported dengue infections from this period were obtained from the National Infectious Disease Register, which is maintained by the Finnish Institute for Health and Welfare (THL). The questionnaire both in Finnish and Swedish was sent to the participants. The response rate in this study was 61.3 %. Data was analyzed spatially with QGIS 3.4.8 Madeira and statistically by using R 3.6.0. Descriptive statistics were used to analyze the demographic variables as well as answers given to the questionnaire. In addition, two binary logistic models were fitted to find out statistically significant factors for risk perception and the use of protective measures. Crude attack rates were calculated for different destinations using UNWTO travel data. Further on, the results were compared to existing literature related to this research. Thailand and Indonesia were identified as destinations with the most abundant number of infections imported to Finland. However, Maldives had the highest crude attack rate per 100,000 travelers. The type of travel during which the infections were acquired was mainly pre-booked holiday of 14 days with time spent mostly on the beach. Most of the travelers were not aware of the dengue risk before the travel and did not seek pre-travel advice. Those who sought pre-travel advice were 34.9 times more likely to use protective measures than those who did not. Moreover, the majority applied some protective measures but not during the right time of the day, and thus the measures were chosen incorrectly. Based on these results the knowledge about dengue, day-active/urban mosquito and the correct use of protective measures needs increasing. Further on, the risk within touristic destinations requires highlighting and the distinction between malaria and other mosquito-borne diseases could be made clearer. In addition, there is a need to increase the knowledge of dengue among healthcare workers