Outbreak Of Dengue Fever Research Articles

Spatial analysis of dengue fever and exploration of its environmental and socio-economic risk factors using ordinary least squares: A case study in five districts of Guangzhou City, China, 2014

ObjectiveSpatial patterns and environmental and socio-economic risk factors of dengue fever have been studied widely on a coarse scale; however, there are few such quantitative studies on a fine scale. There is a need to investigate these factors on a fine scale for dengue fever. MethodsIn this study, a dataset of dengue fever cases and environmental and socio-economic factors was constructed at 1-km spatial resolution, in particular ‘land types’ (LT), obtained from the first high resolution remote sensing satellite launched from China (GF-1 satellite), and ‘land surface temperature’, obtained from moderate resolution imaging spectroradiometer (MODIS) images. Spatial analysis methods, including point density, average nearest neighbor, spatial autocorrelation, and hot spot analysis, were used to analyze spatial patterns of dengue fever. Spearman rank correlation and ordinary least squares (OLS) were used to explore associated environmental and socio-economic risk factors of dengue fever in five districts of Guangzhou City, China in 2014. ResultsA total of 30553 dengue fever cases were reported in the districts of Baiyun, Haizhu, Yuexiu, Liwan, and Tianhe of Guangzhou, China in 2014. Dengue fever cases showed strong seasonal variation. The cases from August to October accounted for 96.3% of the total cases in 2014. The top three districts for dengue fever morbidity were Baiyun (1.32%), Liwan (0.62%), and Haizhu (0.60%). Strong spatial clusters of dengue fever cases were observed. Areas of high density for dengue fever were located at the district junctions. The dengue fever outbreak was significantly correlated with LT, normalized difference water index (NDWI), land surface temperature of daytime (LSTD), land surface temperature of nighttime (LSTN), population density (PD), and gross domestic product (GDP) (correlation coefficients of 0.483, 0.456, 0.612, 0.699, 0.705, and 0.205, respectively). The OLS equation was built with dengue fever cases as the dependent variable and LT, LSTN, and PD as explanatory variables. The residuals were not spatially autocorrelated. The adjusted R-squared was 0.320. ConclusionsThe findings of spatio-temporal patterns and risk factors of dengue fever can provide scientific information for public health practitioners to formulate targeted, strategic plans and implement effective public health prevention and control measures.

GeoSentinel - A surveillance network of returning travelers and migrants

GeoSentinel is an emerging infectious disease network created as a joint project between the International Society of Travel Medicine (ISTM) and the U.S. Centers for Disease Control and Prevention (CDC). GeoSentinel has several major objectives including conducting surveillance for emerging infectious diseases; rapidly sharing novel data on emerging infections with participating sites, internet information services (e.g. ProMED), and public health authorities (e.g. CDC, ECDC, Public Health Agency of Canada); and analyzing, presenting, and publishing surveillance results collaboratively with CDC, GeoSentinel sites, and GeoSentinel's two regional subnetworks, CanTravNet and EuroTravNet. The GeoSentinel surveillance network provides robust data that defines the spectrum of illness and its relation to place of exposure for significant health risks facing travelers and migrants. There are currently 70 GeoSentinel sites in 30 countries and 210 Affiliate Members with a presence on every continent except Antarctica. The GeoSentinel database now contains nearly 300,000 patient records; these include 54% post-travel visits, 31% seen during travel, and 15% migrants. GeoSentinel has successfully identified new outbreaks over the last two decades including leptospirosis in participants in the Borneo Ecochallenge (2000), sarcocystis in travelers to Tionan Island, Malaysia (2010), an outbreak of dengue fever in Angola (2013), schistosomiasis in vacationers in Corsica (2014), and sentinel cases of Zika virus disease in Costa Rica (2016), Vietnam (2016), and several other destinations in Southeast Asia and the South Pacific in recent years. GeoSentinel has also characterized the risk for travelers to countries where major sporting events were planned such as the 2016 Summer Olympics in Brazil. In summary, GeoSentinel is a vibrant global surveillance system that has made important contributions to the identification of disease outbreaks and trends in infectious disease epidemiology among travelers and migrants. In addition, GeoSentinel has the potential, through the development of a biobank of clinical samples, to collaborate with high level laboratories in academia and government to identify new pathogens responsible for respiratory, gastrointestinal, and systemic infections. GeoSentinel translates clinical and epidemiological data into meaningful evidence that is of critical importance to the containment of the global spread of infection.

Knowledge, awareness and attitude towards dengue fever outbreaks in the summer

Dengue fever is a life-threatening viral infection that has been a public health problem. It is estimated that almost more than one half of the world population live in countries where dengue fever is endemic. Among affected individuals, about 5% develop the fatal dengue shock syndrome and dengue hemorrhagic fever, and almost 20,000 patients die annually with these conditions. Despite the magnitude of the problem, dengue fever is a preventable disease. Prevention can be carried out by elimination of inhabitant mosquitoes, vaccination of vulnerable individuals, and regular health education particularly during outbreaks. Many literatures studies were conducted to evaluate the impact of healthcare education on dengue fever prevention, and many researchers studied the population awareness and attitudes about the disease. Knowledge and awareness varied among different studies, and some factors were reported to influence this knowledge such as gender, socioeconomic status, level of education, and computer literacy. The attitudes also differed among the studies and did not seem to be correlated with the population knowledge about the disease. This article will review the knowledge, awareness, and attitudes among different countries towards dengue fever outbreaks in the summer.

Knowledge, Attitude and Practice regarding Dengue Fever among Mothers in Select Dengue Outbreak Villages in Malaybalay City, Bukidnon

Dengue fever (DF) is the most rapidly spreading mosquito-borne viral disease in the world. In this decade, it has expanded to new countries, from urban to rural areas. The main objective of this study was to assess the local knowledge, attitude, and practice (KAP) of DF among mothers in select dengue outbreak villages in Malaybalay City, Bukidnon. A descriptive cross-sectional study was conducted among 280 respondents living in top five dengue hot spot villages. The research protocol was strictly observed in the study. The authors asked permission to make use of the designed questionnaire by Mohapatra and Aslami (2016) on knowledge, attitude and practice regarding DF using. Out of 280 individuals interviewed, 89% identified fever as a cardinal symptom of DF. The knowledge about other symptoms of DF was low among participants. Only 10.8 % knew that DF is transmitted by Aedes mosquitoes. The correct timing of biting time was known by only 7%. Despite low knowledge, the participants had a good attitude and most of them reported good preventive practices against dengue prevention and control. Therefore, there is an urgent need for awareness programs to raise the knowledge of people of this selected dengue fever outbreak villages to protect the health of the people. Keywords : Dengue fever, mothers, out-break villages, KAP Â

Cost-of-Illness and the Health-Related Quality of Life of Patients in the Dengue Fever Outbreak in Hanoi in 2017

Dengue fever (DF) outbreaks occur intermittently in Vietnam, and the most recent epidemic happened in 2017. However, attempts to measure the burden of DF in relation to the quality of life and the cost of treatment for patients during an epidemic period are constrained. This study explored the health-related quality of life (HRQOL) and the cost of illness among patients with dengue fever in Vietnam. A cross-sectional study was conducted in Bach Mai Hospital from September to November 2017. The EuroQol-5 dimensions-5 levels (EQ-5D-5L) was used to measure HRQOL. Cost-of illness was measured by collecting data about the direct medical cost, the non-medical cost, and the indirect cost. Among 225 patients, most of the participants experienced problems regarding mobility (62.3%), self-care (71.8%), usual activities (64.6%), and anxiety/depression (64.1%). The mean EQ-5D index was 0.66 (SD = 0.24). The median cost of illness for inpatient and outpatient groups were US $110.10 (IQR = US $4.40–1200.00) and US $36.10 (IQR = US $1.80–816.30), respectively. Indirect costs accounted for a major proportion in both groups. Lower-skilled workers and those with a higher severity of the disease had significantly lower HRQOL. Meanwhile, people who were inpatients, had comorbidities, had higher incomes, and who experienced a longer disease duration, had a higher cost of treatment. In conclusion, high costs and severe health deterioration, especially in psychological dimensions, were found in patients with DF in Vietnam. Strengthening primary health care services and communication campaigns are necessary to relieve the burden of diseases and could possibly contribute to effective DF control and prevention strategies.

An Epidemiological Study of Dengue Outbreak in Pakistan

<p><em>In 2016, Hazara Division reported his major outbreak of Dengue fever. In this context, current epidemiological and serological survey conducted to highlight the actual burden of Dengue fever in cities of Hazara Division. Blood samples were taken from the total of 1462 suspected people for detection of Dengue antibodies. Among these patients, 1359 (93%) were found to be positive for Dengue, including 965 (71%) males and 394 (29%) females. Distribution in keeping the presence of antibodies shows 897 (66%) IgM positive people.</em><em> </em><em>Second most frequently seen antibodies were both IgG and IgM in 435 (32%) people. Presence of IgG antibodies was detected in 27 (2%) individuals. 1142 (84%) of Dengue positive people were not found to be symptomatic while rest of 217 (16%) observed with various symptoms. In this outbreak peak incidence of Dengue fever was observed in Manshera city. Although minimum was seen in Abbottabad city. To conclude, this might be the largest outbreak in the history of Hazara Division and second in Khyber Pakhtunkhwa. We recommend that policymakers and the government of Khyber Pakhtunkhwa desperately need to make efforts to prevent this mounting ratio of Dengue fever and implement the vector management policies by environmental measures and promote awareness in this area.</em></p>

Complete Coding Sequences of Dengue Virus Type 2 Strains from Febrile Patients Seen in Malindi District Hospital, Kenya, during the 2017 Dengue Fever Outbreak.

ABSTRACTWe report here 10 complete polyprotein-coding sequences of dengue virus type 2 strains isolated from febrile patients who presented at Malindi District Hospital, Kenya, during a recent dengue fever outbreak. Phylogenetically, all the strains belonged to clonal serotype 2 of the Cosmopolitan genotype.

The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012.

Dengue fever, a mosquito-borne arbovirus, is a major public health concern in Ecuador. In this study, we aimed to describe the spatial distribution of dengue risk and identify local social-ecological factors associated with an outbreak of dengue fever in the city of Guayaquil, Ecuador. We examined georeferenced dengue cases (n = 4248) and block-level census data variables to identify social-ecological risk factors associated with the presence/absence and burden of dengue in Guayaquil in 2012. Local Indicators of Spatial Association (LISA), specifically Anselin’s Local Moran’s I, and Moran’s I tests were used to locate hotspots of dengue transmission, and multimodel selection was used to identify covariates associated with dengue presence and burden at the census block level. We identified significant dengue transmission hotspots near the North Central and Southern portions of Guayaquil. Significant risk factors for presence of dengue included poor housing conditions, access to paved roads, and receipt of remittances. Counterintuitive positive correlations with dengue presence were observed with several municipal services such as garbage collection and access to piped water. Risk factors for increased burden of dengue included poor housing conditions, garbage collection, receipt of remittances, and sharing a property with more than one household. Social factors such as education and household demographics were negatively correlated with increased dengue burden. These findings elucidate underlying differences with dengue presence versus burden, and suggest that vulnerability and risk maps could be developed to inform dengue prevention and control; this is information that is also relevant for emerging epidemics of chikungunya and Zika viruses.

Investigation of Dengue Fever Outbreak- District Peshawar, Pakistan, September 2017

Background: About 50 to 100 million people get infected with Dengue every year. Since 2003, Pakistan has seen multiple outbreaks with biggest one in 2011 in Lahore involving 22,000 cases. From August 6 to September 16, 2017, 112 Dengue fever cases were reported to a hospital in town X of District Peshawar.

THE USE OF GEOGRAPHIC INFORMATION SYSTEMS IN THE DETERMINATION OF AREAS AT RISK OF DENGUE IN THE SÃO PAULO STATE

The Dengue Virus is a Flavivirus transmitted to humans by Aedes mosquitoes. This arbovirus is responsible for one of the main problems of global public health, because it is estimated that over 80 million people are infected by it every year. In Brazil there are reports that dengue fever epidemics have been present from the beginning of the twentieth century. However, the first dengue fever outbreak documented in the country occurred in 1982. From that point on, the Brazilian territory has been plagued by several of these epidemics. Among the various attempts to elucidate the factors that influence the dynamics of the transmission of dengue fever, the Brazilian Ministry of Health reorganized, in 2002, the National Plan to Combat Dengue Fever and instituted a normative procedure by which the areas affected by this epidemic should be mapped. The use of geographical information systems is a powerful tool for the spatial analysis of risk; by this means the spatial distribution of the event can be described and associations between an event and its determinants can be observed. Based on these assumptions this study aims to determine the geospatial patterns of dengue fever outbreaks in S„o Paulo State, Brazil. This region has been seriously affected by successive epidemics during the last 15 years. We have used conventional and spatial analytical tools (geostatistics analysis) to discern the patterns of the epidemic scenarios. Results showed that dengue fever outbreaks have affected two principal areas of risk: one located in the northwest and the other in the central-south portion of the S„o Paulo State

Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico.

Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Epidemiological, clinical and climatic characteristics of dengue fever in Kaohsiung City, Taiwan with implication for prevention and control.

BackgroundThe early identification of dengue infection is essential for timely and effective quarantine and vector control measures for preventing outbreaks of the disease. Kaohsiung City is responsible for most of the dengue cases in Taiwan. Thus, this study aims to identify major factors involved in the prevalence of dengue fever by analyzing the epidemiological and clinical characteristics, and to establish associations between weather parameters and dengue occurrence in this City.MethodsA retrospective study was conducted with 3,322 confirmed dengue cases. Appropriate statistical methods were used to compare differences and correlations between dengue occurrence and demographic, clinical and weather parameters.ResultsThe outbreak of dengue fever was found to be initiated by imported cases of dengue viruses from other endemic countries. Most of the confirmed cases were not reported to the health authority during the first visit to a doctor, and it took a median of 5 days after the appearance of the first syndromes for medical personnel to report suspected dengue cases. Accordingly, Aedes mosquitoes would have enough time to be infected and transmit the dengue virus. The diagnosis and notification criteria should not only include common symptoms of fever, myalgia, headache, skin rash and arthralgia, but should also be adjusted to include the most frequent symptoms of loss of appetite and feeling thirsty to shorten the notification time. Significantly positive correlations were found between the number of confirmed cases and weather parameters (i.e., temperature, rainfall and relative humidity) at a time lag of 1 month and 2 months. The predictive models for dengue occurrence using these three parameters at a 2-month lag time were established.ConclusionsThe surveillance of imported cases, adjustment of notification criteria and application of climatic predictive models would be helpful in strengthening the dengue early warning surveillance system.

Historical Perspective of Arboviruses in Mozambique and Its Implication for Current and Future Epidemics.

Mozambique is a tropical country situated in the Southern part of Africa, a region where data on the burden and epidemiology of arbovirus is presently quite scarce although the frequency of outbreaks caused by arboviruses is rapidly increasing. Outbreaks of dengue fever have been reported in Mozambique, Angola and Tanzania and a recent unprecedented outbreak of Yellow fever has been recorded in Angola. These new outbreaks collectively suggest that arboviruses, and specifically flavivirus infections, are endemic in Mozambique.Although recent data on arbovirus activity is scarce, the work of Kokernot et al. [R.H. Kokernot, K.C. Smithburn, A.F. Gandara, B.M. Mc'Intosh and C.S. Heymann Anais Inst Med Trop (1960), 17:201-230] describes seroepidemiological and entomological studies carried out in several parts of Mozambique during the 1950s. Complementary seroepidemiological investigations on arboviruses that were conducted in the early 1980s also found serological evidence of several arboviruses which included Dengue, Chikungunya, Zika, Rift Valey Fever, Sinbdis virus, Wesselsbron, Bunyamwera, Pongola and Bawamba Fever and Yellow Fever.Notably the first description of Chikungunya virus in 1952-1953 in Tanzania also included reported cases in northern Mozambique. Furthermore, DENV serotype 3 was for the first time described in northern Mozambique in 1984 and 1985. Since several arboviral infections result in acute self limiting fever they have remained unsuspected for several decades. However, it is well known that during the 1980's intensive malaria control initiatives which included massive distribution of bed nets, community education and indoor and outdoor spraying campaigns were implemented. It is possible that these measures may have influenced the epidemiology of arboviruses. However, the impact of these interventions in controlling the spread of arboviruses is not known.In conclusion, the old literature on arboviruses in Mozambique is relevant for assessing the gaps and current risk of occurrence of these pathogens at the region, particularly in a time in which they are spreading worldwide.

A Case Report of Chikungunya Versus Dengue During an Acute Outbreak of Dengue Fever in The Solomon Islands, 2013

A Case Report of Chikungunya Versus Dengue During an Acute Outbreak of Dengue Fever in The Solomon Islands, 2013

Using Public Open Data to Predict Dengue Epidemic: Assessment of Weather Variability, Population Density, and Land Use As Predictor Variables For Dengue Outbreak Prediction using Support Vector Machine

Objectives: This study was performed to predict dengue outbreaks using predictor variables derived from weather variability, population density, land use and elevation in Malaysia. Methods: We used publicly available data associated with dengue from the Malaysian open data platform and historical dengue case data from the Ministry of Health Malaysia. We investigated the correlations between predictor variables related to Weather Variability, Population Density, Residential Building Types and Construction Sites; with that of outbreaks of dengue in the chosen site of study, and used the Support Vector Machine classifier to predict outbreak of dengue fever. Results: The model we proposed was evaluated through cross-validation, and returned an accuracy rate of 88.62%, while sensitivity was 93%, and specificity was recorded at 79.32%. Population density had the highest correlation with predicted dengue fever outbreak. Conclusions: In this study, we assessed weather variability, population density, and land use as predictor variables for predicting dengue fever outbreak using a Support Vector Machine classifier. Keywords: Dengue, Infectious Disease, Meteorology, Projection and Predictions, Support Vector Machine

Trend of morbidity and mortality of dengue in Tamil Nadu and Puducherry, South India

Background: Globally, dengue infection is a mosquito borne viral disease accounts for nearly 50 million cases per year. South East Asia (SEA), Western Pacific, Africa, Eastern Mediterranean and Latin America are all endemic for frequent outbreaks of dengue fever. The burden of dengue is 17 times higher in SEA countries as compared to Japanese encephalitis, upper respiratory tract infections and Hepatitis B. Thus a study was planned to assess the trend of morbidity and mortality of dengue for the period of five years from 2012 to 2016 in Puducherry and Tamil Nadu (TN), South India. Methods: The data were sourced from National Health Profile 2017, WHO vector borne disease fact sheets and official website of National Vector Borne Disease Control Program. Analysis of secondary data was done and results were tabulated. Graphs were plotted to study the trend of the disease. Results: There is a fall in the morbidity of Dengue cases whereas increase in the mortality over the period of five years (2012-2016) in Puducherry. In TN, there is a decrease in trend of morbidity over the past five years and mortality since 2015. However, there is a rising trend of disease in India. Conclusions: Considering the above facts there is no doubt that dengue is a public health problem in Puducherry and Tamil Nadu, which needs urgent action to reduce the burden of dengue.

Exploring Determinants of Spatial Variations in the Dengue Fever Epidemic Using Geographically Weighted Regression Model: A Case Study in the Joint Guangzhou-Foshan Area, China, 2014.

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, this imported disease has posed an increasing threat to public health in China, especially in many southern cities. Although the severity of DF outbreaks in these cities is generally associated with known risk factors at various administrative levels, spatial heterogeneities of these associations remain little understood on a finer scale. In this study, the neighboring Guangzhou and Foshan (GF) cities were considered as a joint area for characterizing the spatial variations in the 2014 DF epidemic at various grid levels from 1 × 1 km2 to 6 × 6 km2. On an appropriate scale, geographically weighted regression (GWR) models were employed to interpret the influences of socioeconomic and environmental factors on this epidemic across the GF area. DF transmissions in Guangzhou and Foshan cities presented synchronous temporal changes and spatial expansions during the main epidemic months. Across the GF area, this epidemic was obviously spatially featured at various grid levels, especially on the 2 × 2 km2 scale. Its spatial variations were relatively sufficiently explained by population size, road density, and economic status integrated in the GWR model with the lowest Akaike Information Criterion (AICc = 5227.97) and highest adjusted R square (0.732) values. These results indicated that these three socioeconomic factors acted as geographical determinants of spatial variability of the 2014 DF epidemic across the joint GF area, although some other potential factors should be added to improve the explaining the spatial variations in the central zones. This work improves our understanding of the effects of socioeconomic conditions on the spatial variations in this epidemic and helps local hygienic authorities to make targeted joint interventions for preventing and controlling this epidemic across the GF area.

Clinico-epidemiological study of dengue cases in a tertiary care hospital, Guwahati, Assam, India

Background: Dengue fever (DF) is a common mosquito borne disease caused by dengue virus and is transmitted by Aedes mosquito. It is one of the major public health problems in India. A large-scale outbreak of dengue fever occurred in 2016 involving several districts of Assam. Here we report our experience with Dengue cases. The present study was aimed to assess clinical and epidemiological aspects of dengue cases presenting in down town hospital, Guwahati, Assam.Methods: A record based retrospective study conducted in our centre. Total 880 fever cases, fulfilling WHO criteria for dengue suspicion were included in our study. Required data from the entire laboratory confirmed cases from 1st January to 31st December 2016 were collected from MRD (medical record department) and analysed. Epidemiological data were compared with previous year’s available data from 1st January to 31st December 2015.Results: The number of dengue cases in 2016 clearly outnumbered the dengue cases in past years. Most of the cases were in the age group 20-50 years with a male preponderance. The outbreak occurred during the months of August-November indicating increased vector transmission in the monsoon and post monsoon periods. Average days of hospital stay were 6 days.Conclusions: Measures can be taken both at personal and government level to reduce morbidity and mortality from dengue particularly during the monsoon period.

The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infectious diseases

BackgroundThe mosquito Aedes aegypti has long been a vector for human illness in the Southeastern United States. In the past, it has been responsible for outbreaks of dengue, chikungunya, and yellow fever and, very recently, the Zika virus that has been introduced to the region. Multiple studies have modeled the geographic distribution of Ae. aegypti as a function of climate factors; however, this ignores the importance of humans to the anthropophilic biter. Furthermore, Ae. aegypti thrives in areas where humans have created standing water sites, such as water storage containers and trash. As models are developed to examine the potential impact of climate change, it becomes increasingly important to include the most comprehensive set of predictors possible.ResultsThis study uses Maxent, a species distribution model, to evaluate the effects of adding poverty and population density to climate-only models. Performance was evaluated through model fit statistics, such as AUC, omission, and commission, as well as individual variable contributions and response curves. Models which included both population density and poverty exhibited better predictive power and produced more precise distribution maps. Furthermore, the two human population characteristics accounted for much of the model contribution—more so than climate variables.ConclusionsModeling mosquito distributions without accounting for their dependence on local human populations may miss factors that are very important to niche realization and subsequent risk of infection for humans. Further research is needed to determine if additional human characteristics should be evaluated for model inclusion.

Enfermedades zoonóticas virales emergentes. Importancia ecológica y su evaluación en el sureste de México

Es de gran importancia conocer y evaluar las enfermedades zoonóticas emergentes que existen en el sureste de México, ya que representan una amenaza significativa para la salud pública. Los cambios antropogénicos, por ejemplo, la deforestación, el establecimiento de monocultivos, el aumento de la urbanización y la densidad poblacional humana son responsables de la mayoría de las enfermedades infecciosas emergentes zoonóticas. Pronósticos de eventos emergentes indican que en los países con ambientes tropicales (como México) existe una mayor diversidad de zoonosis transmitidas por animales silvestres, por lo que debemos contener y dar rápida respuesta ante posibles brotes de dengue hemorrágico y otras fiebres hemorrágicas virales (FHV). Es importante contar con datos actualizados de las posibles áreas de distribución y ecosistemas de las especies de mamíferos (en particular roedores y murciélagos) que potencialmente pueden ser reservorios o vectores de varias de las FHV, crear modelos con información base para contener brotes de FHV, así como determinar los cambios en los ambientes y la distribución de especies afectadas por actividades humanas.