Dataset Information

A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

ABSTRACT:

Background

In Pakistan, dengue fever has become a major concerning factor, given that it is a relatively new disease compared to malaria. The number of people affected by dengue fever has increased at least 10-fold in the last 15 years in specific areas of Pakistan. Therefore, it is necessary to analyse this disease to reduce or prevent the effects of dengue fever in the region.

Methods

Geographical information system (GIS) maps are used to identify the intensity of the spread according to the count of affected people in our study area. Generalised linear modelling (GLM) is used to study the significance of factors associated with dengue fever.

Results

The dengue virus is present throughout the year in specific areas of Pakistan. Karachi and Lahore are most significantly affected with cases in these two most populous cities of Pakistan reported every year. In the study period (2006-2017), 2011 was the most devastating year for Pakistan. Lahore recorded more than 17,000 confirmed cases with 290 deaths in a single year. The GLM analysis shows rainfall, the average maximum temperature, and hospitals to be significant factors in the prevalence of dengue fever.

Conclusion

This study finds that Sindh and Khyber Pakhtunkhwa are two of the primarily vulnerable provinces for the spread of dengue fever. Punjab has observed sporadic increases in dengue fever cases. In Pakistan, dengue cases increase in the rainfall season, especially during monsoon season. Lack of proper hospitals and clinics are another major factor, and mobile hospitals are needed in remote hotspot regions often affected by dengue fever. Finally, improved sanitation systems in metropoles would facilitate reducing breeding grounds for Aedes Aegypti larvae.

SUBMITTER: Shabbir W

PROVIDER: S-EPMC7318413 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

Shabbir Waqas W Pilz Juergen J Naeem Amna A

BMC public health 20200625 1

<h4>Background</h4>In Pakistan, dengue fever has become a major concerning factor, given that it is a relatively new disease compared to malaria. The number of people affected by dengue fever has increased at least 10-fold in the last 15 years in specific areas of Pakistan. Therefore, it is necessary to analyse this disease to reduce or prevent the effects of dengue fever in the region.<h4>Methods</h4>Geographical information system (GIS) maps are used to identify the intensity of the spread acc ...[more]

PMID: 32586294

Similar Datasets

Project description:BackgroundDengue, a viral disease transmitted by Aedes mosquitoes, is an important public health concern throughout Thailand. Climate variables are potential predictors of dengue transmission. Associations between climate variables and dengue have usually been performed on large-scale first-level national administrative divisions, i.e. provinces. Here we analyze data on a finer spatial resolution in one province, which is often more relevant for effective disease control design. The objective of this study was to investigate the effect of seasonal variations, monthly climate variability, and to identify local clusters of symptomatic disease at the sub-district level based on reported dengue cases.MethodsData on dengue cases were retrieved from the national communicable disease surveillance system in Thailand. Between 2006 and 2016, 15,167 cases were recorded in 199 sub-districts of Khon Kaen Province, northeastern Thailand. Descriptive analyses included demographic characteristics and temporal patterns of disease and climate variables. The association between monthly disease incidence and climate variations was analyzed at the sub-district level using Bayesian Poisson spatial regression. A hotspot analysis was used to assess the spatial patterns (clustered/dispersed/random) of dengue incidence.ResultsDengue was predominant in the 5-14?year-old age group (51.1%). However, over time, dengue incidence in the older age groups (>?15?years) gradually increased and was the most affected group in 2013. Dengue outbreaks coincide with the rainy season. In the spatial regression model, maximum temperature was associated with higher incidence. The hotspot analysis showed clustering of cases around the urbanized area of Khon Kaen city and in rural areas in the southwestern portion of the province.ConclusionsThere was an increase in the number of reported dengue cases in older age groups over the study period. Dengue incidence was highly seasonal and positively associated with maximum ambient temperature. However, climatic variables did not explain all the spatial variation of dengue in the province. Further analyses are needed to clarify the detailed effects of urbanization and other potential environmental risk factors. These results provide useful information for ongoing prediction modeling and developing of dengue early warning systems to guide vector control operations.

Project description:BACKGROUND: Even though dengue has been recognized as one of the major public health threats in Pakistan, the understanding of its molecular epidemiology is still limited. The genotypic diversity of Dengue virus (DENV) serotypes involved in dengue outbreaks since 2005 in Pakistan is not well studied. Here, we investigated the origin, diversity, genetic relationships and geographic distribution of DENV to understand virus evolution during the recent expansion of dengue in Pakistan. METHODS: The study included 200 sera obtained from dengue-suspected patients from 2006 to 2011. DENV infection was confirmed in 94 (47%) sera by a polymerase chain reaction assay. These included 36 (38.3%) DENV-2, 57 DENV-3 (60.6%) and 1 DENV-4 (1.1%) cases. Sequences of 13 whole genomes (6 DENV-2, 6 DENV-3 and 1 DENV-4) and 49 envelope genes (26 DENV-2, 22 DENV-3 and 1 DENV-4) were analysed to determine the origin, phylogeny, diversity and selection pressure during virus evolution. RESULTS: DENV-2, DENV-3 and DENV-4 in Pakistan from 2006 to 2011 shared 98.5-99.6% nucleotide and 99.3-99.9% amino acid similarity with those circulated in the Indian subcontinent during the last decade. Nevertheless, Pakistan DENV-2 and DENV-3 strains formed distinct clades characterized by amino acid signatures of NS2A-I116T + NS5-K861R and NS3-K590R + NS5-S895L respectively. Each clade consisted of a heterogenous virus population that circulated in Southern (2006-2009) and Northern Pakistan (2011). CONCLUSIONS: DENV-2, DENV-3 and DENV-4 that circulated during 2006-2011 are likely to have first introduced via the southern route of Pakistan. Both DENV-2 and DENV-3 have undergone in-situ evolution to generate heterogenous populations, possibly driven by sustained local DENV transmission during 2006-2011 periods. While both DENV-2 and DENV-3 continued to circulate in Southern Pakistan until 2009, DENV-2 has spread in a Northern direction to establish in Punjab Province, which experienced a massive dengue outbreak in 2011.

Project description:BACKGROUND:Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS:Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS:Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION:The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

Project description:BackgroundRabies is a zoonotic viral disease that can affect all mammals. In the United States, the majority of human rabies cases are caused by bats, which are the only known reservoirs for rabies virus (RABV) in Washington State. We sought to characterize bat RABV epidemiology in Washington among bats submitted by the public for RABV testing.MethodsWe examined temporal and spatial trends in RABV positivity (% positive) for taxonomically identified bats submitted to diagnostic laboratories during 2006-2017. For a subset of Myotis species, we evaluated sensitivity and predictive value positive (PPV) of morphological identification keys, using mitochondrial markers (cytochrome b) as a reference. For bats tested during 2000-2016, we analyzed RABV positivity by circumstances of encounters with humans, cats, and dogs.ResultsDuring 2006-2017, RABV positivity for all bat species was 6.0% (176/2,928). Among species with ≥100 submissions, RABV positivity was 2.0%-11.7% and highest among big brown bats (Eptesicus fuscus). An increasing trend in annual positivity was significant only for big brown bats (P = 0.02), and was circumstantially linked to a geographic cluster. Sensitivity and PPV of morphological identification keys was high for M. evotis but varied for M. lucifugus, M. californicus, M. yumanensis, and M. septentrionalis. A positive RABV result was significantly associated with nonsynanthropic species, abnormal behavior, abnormal hiding, injury, biting, found in a body of water, found alive, found outdoors, and caught by a dog.ConclusionMonitoring passive RABV surveillance trends enables public health authorities to perform more accurate risk assessments. Differences in temporal and spatial trends in RABV positivity by bat species indicate the importance of collecting taxonomic data, although morphological identification can be unreliable for certain Myotis species. Current public health practices for RABV exposures should be maintained as RABV infection in bats can never be excluded without diagnostic testing.

Project description:Background/objectivesUnderstanding the factors underlying the spatio-temporal distribution of infectious diseases provides useful information regarding their prevention and control. Dengue fever spatio-temporal patterns result from complex interactions between the virus, the host, and the vector. These interactions can be influenced by environmental conditions. Our objectives were to analyse dengue fever spatial distribution over New Caledonia during epidemic years, to identify some of the main underlying factors, and to predict the spatial evolution of dengue fever under changing climatic conditions, at the 2100 horizon.MethodsWe used principal component analysis and support vector machines to analyse and model the influence of climate and socio-economic variables on the mean spatial distribution of 24,272 dengue cases reported from 1995 to 2012 in thirty-three communes of New Caledonia. We then modelled and estimated the future evolution of dengue incidence rates using a regional downscaling of future climate projections.ResultsThe spatial distribution of dengue fever cases is highly heterogeneous. The variables most associated with this observed heterogeneity are the mean temperature, the mean number of people per premise, and the mean percentage of unemployed people, a variable highly correlated with people's way of life. Rainfall does not seem to play an important role in the spatial distribution of dengue cases during epidemics. By the end of the 21st century, if temperature increases by approximately 3 °C, mean incidence rates during epidemics could double.ConclusionIn New Caledonia, a subtropical insular environment, both temperature and socio-economic conditions are influencing the spatial spread of dengue fever. Extension of this study to other countries worldwide should improve the knowledge about climate influence on dengue burden and about the complex interplay between different factors. This study presents a methodology that can be used as a step by step guide to model dengue spatial heterogeneity in other countries.

Project description:Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse-resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine-grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold-air drainage, coastal exposure and elevation) were determined from the coarse-resolution climate surfaces using thin-plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983-2017 and 2041-2049. Results indicated that there is a fine-resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High-temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so.

Dataset Information

A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

Background

Methods

Results

Conclusion

Publications

A spatial-temporal study for the spread of dengue depending on climate factors in Pakistan (2006-2017).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure