1,137 research outputs found

    Using GIS to map the spatial and temporal occurrence of cholera epidemic in Camaroon

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    Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial TechnologiesGlobally, Cholera has been a major infectious disease due to its intercontinental, environmental and cultural factors. This study focused on evaluating the climatic and fresh water proximity factors influencing Cholera epidemic in Cameroon. To this effect, Cholera and climatic datasets in 2004, 2010, 2011 and 2012 to June 2013 were collected and mapped. Both high and low rainfall and temperature extremes were designated as promoters of V. Cholerae development and the highest cases were identified in the Littoral, Extreme North and Centre regions. Spatial autocorrelation using Local (Anselin) Moran I on Cholera cases revealed a cluster of Low-Low positive autocorrelation in Adamawa region in 2004, a High-High cluster of positive autocorrelation in the Littoral region and a Low-High negative autocorrelation in the South region in 2012, a Low-High negative autocorrelation in the South West region and a High-Low negative autocorrelation in the North West in 2013. Furthermore, using population numbers to count Cholera cases (prevalence) from 2010 to June 2013, Local Moran I results show a Low-Low cluster of positive autocorrelation in the South region, a Low-High negative autocorrelation in the North region and a High-Low negative autocorrelation in the Adamawa region in 2010, a High-Low negative spatial autocorrelation in the North region in 2011, a High-Low negative spatial autocorrelation in the South region in 2012 and a High-Low negative spatial autocorrelation in the North region in 2013. Spatial Poisson Regression analysis allowed concluding that Average Temperature, Distance to Streams, Population Distribution and Latitude are statistically significant predictors of increased Cholera cases, whereas Average Rainfall and Longitude are significant predictors of lower Cholera cases

    Characterizing Multiple Spatial Waves of the 1991-1997 Cholera Epidemic in Peru

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    Background Due to a lack of sanitary infrastructure and a highly susceptible population, Peru experienced a historic outbreak of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease for several years. Though case-fatality was low, the epidemic put massive strain on healthcare and governmental resources. Here we explore the transmission dynamics and spatiotemporal variation of cholera in Peru using mathematical models and statistical analyses that account for environmental conditions favoring the persistence of bacteria in the environment. Methods The authors use dynamic transmission models that incorporate seasonal variation in temperature, concentration of vibrios in the environment, as well as separate human and environmental transmission pathways. The model is fit to weekly department level data obtained from the cholera surveillance system in Peru. The authors also assess the spatial patterns of cholera transmission and correlations between case incidence, time of epidemic onset, and department level variables. Reproductive numbers are compared across departments. Results Our findings indicate that the epidemic first hit the coastal departments of Peru and later spread through the highlands and jungle regions. There was high seasonal variation in case incidence, with three clear waves of transmission corresponding to the warm seasons in Peru. Department level variables such as population size and elevation also played a role in transmission patterns. Finally, basic reproductive numbers most often ranged from one to eleven depending on department and time of year. Lima had the largest reproductive number, likely due to its population density and proximity to the coast. Conclusions Incorporating environmental variables into an epidemic model predicts the multiple waves of transmission characteristic of \textit{V. cholerae}, and effectively differentiates transmission patterns by geographic region even in the absence of unique parameter estimates. Mathematical models can provide valuable information about transmission patterns and should continue to be used to inform public health decision making

    Remote sensing based predictive decision support system for assessment of environmental conditions for epidemic cholera

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    Cholera, a water borne diarrheal disease, is strongly associated with environmental processes. However, quantitative linkages of environmental processes with cholera and climate is still emerging. Linking diseases such as cholera with climate will aid in development of models that can predict spatial and temporal outbreaks in resource constrained regions of the globe. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of the disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera.;Conditions for occurrence of cholera were detectable at least one month in advance in several regions of Africa and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature and precipitation. In order to ascertain the integrity of the hypothesis, there are four epidemic regions were picked in Africa with different outbreak\u27s time: Mozambique, Central African Republic, Cameroon and Rwanda. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over selected areas

    Temperature Influence on the Optical Properties, Attenuation Coefficient, and Total Molecular Cross Section of Dhunge Dhara Drinking Water

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    This work aims to measure the different parameters of Dhunge Dhara water (DDW) such as absorbance, transmittance, mass attenuation coefficient (MAC), and molecular cross section (MCS) and experimentally compare the obtained values with those of pure water (PW) at various temperatures (5 °C to 90 °C) using a theremino spectrometer. Observation shows that the parameters vary with temperature and wavelength. The transmittance of DDW ranges from 18% to 85% and absorbance of the same ranges from 0.09 Au to 0.7 Au. Meanwhile, the transmittance of PW ranges from 40% to 98% and the absorbance of the same ranges from 0.09 Au to 0.39 Au. The MAC of PW ranges from 0.02 cm2g−1 to 0.6 cm2g−1, and that for DDW ranges from 0.2 cm2g−1 to 1.1 cm2g−1 at 30 °C. The MCS of PW ranges from to , and that of DDW ranges from to at 30 °C. In conclusion, DDW has an extremely high amount of impurities and total dissolved solids and is recommended to be filtered prior to use (drinking and cooking

    Reconstructing the 1855 cholera epidemic in Basel using geographic information visualization

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    Diese Masterarbeit rekonstruiert die Choleraepidemie in Basel im Jahr 1855 mithilfe geographischer Visualisierungen. Anhand von Karten und Grafiken wurden die rĂ€umlichen und zeitlichen Muster des Choleraausbruchs sowie mögliche Einflussfak- toren wie Topographie, FlusslĂ€ufe und Trinkwasserquellen analysiert. Die in dieser Arbeit verwendeten Daten bestehen aus historischen Archivquellen zu CholerafĂ€llen, die digitalisiert und geokodiert wurden. Weitere historische und zeitgenössische Quellen wie VolkszĂ€hlungsdaten aus den Jahren 1850 und 1860, his- torische Karten und Geodaten zu möglichen Einflussfaktoren wurden aufbereitet, um die UmstĂ€nde der Choleraepidemie zu bestimmen. Die Daten wurden in einem geografischen Informationssystem erfasst und mit Hilfe von Karten und Diagrammen visualisiert. Insgesamt konnten 382 der 399 erfassten CholerafĂ€lle den jeweiligen Adressen in der Stadt Basel zugeordnet werden. Die daraus resultierenden Karten zeigen eine HĂ€ufung von CholerafĂ€llen im Kleinbasel, insbesondere an der Rheingasse in der NĂ€he eines Trinkwasserbrunnens. In Grossbasel traten die FĂ€lle gehĂ€uft vor allem im Birsig-Tal auf, wĂ€hrend es in den höhergelegenen Teilen der Stadt weniger FĂ€lle gab. Die Ergebnisse reihen sich ein in die aktuelle Choleraforschung, die verunreinigtes Trinkwasser, Höhenlage und FlussnĂ€he als Risikofaktoren fĂŒr die Ansteckung mit der Cholera einstuft. Mit dieser Digitalisierung und Aufbereitung der historischen Daten können weitere Forschungen durchgefĂŒhrt werden, z.B. mit Hilfe von Raumanalyse und Statistik oder der Epidemiologie. This Master’s thesis reconstructs the cholera epidemic of Basel in the year 1855 by means of geographic information visualization. The spatial and spatio-temporal patterns of the cholera outbreak were analyzed using maps and graphs along with po- tential influencing factors such as topography, river courses, and sources of drinking water. The data used in this thesis consist of historical archive sources on cholera cases that were digitized and geocoded. Additional historical and contemporary sources such as census data from the years 1850 and 1860, historical maps, and geodata on possible influencing factors were processed to approximate the era of the cholera epidemic. The datasets were collected in a geographic information system and were explored and visualized using maps and graphs. In total, 382 of the 399 recorded cholera cases were assigned to their respective ad- dresses in the city of Basel. The resulting maps revealed cholera case accumulations in Kleinbasel, especially on Rheingasse street in the surroundings of a drinking water well. In Grossbasel, cases occurred in clusters mostly in the Birsig river valley with fewer cases in the more elevated parts of the city. The results stand in relation to contemporary cholera research classifying contami- nated drinking water, elevation, and river proximity as risk factors in the contraction of cholera (World Health Organization 2022, Luque Fernandez et al. 2012, X. Wang and Yang 2021). With this digitization and processing of the historical data, further research can be conducted, e.g. using spatial analysis and statistics, or epidemiology

    COVID-19: a Simple Curve Approximation Tool

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    In the current COVID-19 pandemic, much focus is put on ‘flattening the curve’. This ‘curve’ refers to the cases versus time graph, which shows the rise of a disease to its peak before descending. The aim in a pandemic is to flatten this curve by reducing the peak and spreading out the timeline. However, the models used to predict this curve are often not clearly outlined, no model parameters are given, and models are not tested against real data. This lack of detail makes it difficult to recreate the curve from these models. What is much needed is a simple tool for approximating the curve to allow ideas to be tested and comparisons made. This work presents a simple curve drawing tool which can be used by anyone. This tool allows the user to approximate and draw the curve and allows testing of assumptions, trajectories and the wildly varying figures reported in the media. The mathematics behind this tool is clearly outlined here but understanding of this is not required. The tool itself is provided online as a downloadable MS Excel workbook with some sample cases shown. Throughout this work, the parameters used are specified so that all results can be easily reproduced. Although not intended as a prediction tool it has achieved less than 1% error in short-term forward prediction. It also shows a very significant improvement on the exponential approximations found throughout media reporting. As a comparison tool, it highlights obvious differences between COVID-19 and other diseases, such as influenza, and between countries at different stages of the virus (China, Italy and the UK are used here for demonstration purposes). This drawing tool allows for quick approximation of the curve and creates meaningful comparisons and understandable visualisations for COVID-19 and other pandemics

    Malarial pathocoenosis: beneficial and deleterious interactions between malaria and other human diseases

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    International audienceIn nature, organisms are commonly infected by an assemblage of different parasite species or by genetically distinct parasite strains that interact in complex ways. Linked to co-infections, pathocoenosis, a term proposed by M. Grmek in 1969, refers to a pathological state arising from the interactions of diseases within a population and to the temporal and spatial dynamics of all of the diseases. In the long run, malaria was certainly one of the most important component of past pathocoenoses. Today this disease, which affects hundreds of millions of individuals and results in approximately one million deaths each year, is always highly endemic in over 20% of the world and is thus co-endemic with many other diseases. Therefore, the incidences of co-infections and possible direct and indirect interactions with Plasmodium parasites are very high. Both positive and negative interactions between malaria and other diseases caused by parasites belonging to numerous taxa have been described and in some cases, malaria may modify the process of another disease without being affected itself. Interactions include those observed during voluntary malarial infections intended to cure neuro-syphilis or during the enhanced activations of bacterial gastro-intestinal diseases and HIV infections. Complex relationships with multiple effects should also be considered, such as those observed during helminth infections. Moreover, reports dating back over 2000 years suggested that co-and multiple infections have generally deleterious consequences and analyses of historical texts indicated that malaria might exacerbate both plague and cholera, among other diseases. Possible biases affecting the research of etiological agents caused by the protean manifestations of malaria are discussed. A better understanding of the manner by which pathogens, particularly Plasmodium, modulate immune responses is particularly important for the diagnosis, cure, and control of diseases in human populations

    Seasonality in cholera dynamics : A rainfall-driven model explains the wide range of patterns of an infectious disease in endemic areas

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    An explanation for the spatial variability of seasonal cholera patterns has remained an unresolved problem in tropical medicine. No simple and uniïŹed theory based on local climate variables has been formulated, leaving our understanding of seasonal variations of cholera outbreaks in diïŹ€erent regions of the world incomplete. A mechanistic model for the Bengal region, which encompasses the variety of seasonal patterns worldwide, may provide a unique opportunity to gain insights on the conditions and factors responsible for endemicity around the globe, and therefore, to also revise our understanding of the ecology of Vibrio cholerae. Through the analysis of a unique historical dataset, we propose the ïŹrst mechanistic, rainfall-driven, SIR-based stochastic model we are aware of for the population dynamics of cholera, capable of capturing the full range of seasonal patterns in this large estuarine region. Parameter inference was implemented via new statistical methods that allow the computation of maximum-likelihood estimates for partially observed Markov processes through sequential Monte-Carlo. The results indicate that the hydrological regime is a decisive driver determining the seasonal dynamics of cholera. It was found that rainfall and longer water residence times tend to buïŹ€er the propagation of the disease in wet regions due to a dilution eïŹ€ect, while also enhancing cholera incidence in dry regions. This indicates that overall water levels matter and appear to determine whether the seasonality is unimodal or bimodal, as well as whether it is pre-, post-, or in-phase with the monsoon. We present evidence that the environmental reservoir is responsible for the persistence of the disease, and therefore its endemicity. Given the undeniable interplay between the seasonality of cholera and the environment, a deeper understanding of the underlying mechanisms could allow for the better management and planning of public health policies with respect to climate. In terms of disease prevention and mitigation strategies this is of paramount importance today, as changes in the population dynamics of infectious diseases are expected in response to fast anthropogenic climate change
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