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  • 1
    Language: English
    Description: Although treatment for cholera is well-known and cheap, outbreaks in epidemic regions still exact high death tolls mostly due to the unpreparedness of health care infrastructures to face unforeseen emergencies. In this context, mathematical models for the prediction of the evolution of an ongoing outbreak are of paramount importance. Here, we test a real-time forecasting framework that readily integrates new information as soon as available and periodically issues an updated forecast. The spread of cholera is modeled by a spatially-explicit scheme that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. The framework presents two major innovations for cholera modeling: the use of a data assimilation technique, specifically an ensemble Kalman filter, to update both state variables and parameters based on the observations, and the use of rainfall forecasts to force the model. The exercise of simulating the state of the system and the predictive capabilities of the novel tools, set at the initial phase of the 2010 Haitian cholera outbreak using only information that was available at that time, serves as a benchmark. Our results suggest that the assimilation procedure with the sequential update of the parameters outperforms calibration schemes based on Markov chain Monte Carlo. Moreover, in a forecasting mode the model usefully predicts the spatial incidence of cholera at least for one month ahead. The performance decreases for longer time horizons yet allowing sufficient time to plan for deployment of medical supplies and staff, and to evaluate alternative strategies of emergency management.
    Keywords: Epidemiological Model ; Data Assimilation ; Cholera ; Rainfall Forecast ; Climate Forecast System
    ISSN: 03091708
    E-ISSN: 18729657
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  • 2
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 07 June 2016, Vol.113(23), pp.6421-6
    Description: The spatiotemporal evolution of human mobility and the related fluctuations of population density are known to be key drivers of the dynamics of infectious disease outbreaks. These factors are particularly relevant in the case of mass gatherings, which may act as hotspots of disease transmission and spread. Understanding these dynamics, however, is usually limited by the lack of accurate data, especially in developing countries. Mobile phone call data provide a new, first-order source of information that allows the tracking of the evolution of mobility fluxes with high resolution in space and time. Here, we analyze a dataset of mobile phone records of ∼150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into a spatially explicit, dynamic epidemiological framework. Our model, which also takes into account other drivers of disease transmission such as rainfall, is applied to the 2005 cholera outbreak in Senegal, which totaled more than 30,000 reported cases. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak, had on the course of the epidemic. Such an effect could not be explained by classic, static approaches describing human mobility. Model results also show how concentrated efforts toward disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak.
    Keywords: Cholera Epidemics ; Mobile Phone Call Data ; Spatially Explicit Epidemiological Models ; Waterborne Disease ; Cell Phone ; Disease Outbreaks ; Models, Theoretical ; Population Density ; Cholera -- Epidemiology
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 3
    Language: English
    In: The Journal of infectious diseases, 24 August 2018, Vol.218(7), pp.1164-1168
    Description: Targeted interventions have been delivered to neighbors of cholera cases in major epidemic responses globally despite limited evidence for the impact of such targeting. Using data from urban epidemics in Chad and Democratic Republic of the Congo, we estimate the extent of spatiotemporal zones of increased cholera risk around cases. In both cities, we found zones of increased risk of at least 200 meters during the 5 days immediately after case presentation to a clinic. Risk was highest for those living closest to cases and diminished in time and space similarly across settings. These results provide a rational basis for rapidly delivering targeting interventions.
    Keywords: Disease Outbreaks ; Epidemics ; Cholera -- Epidemiology ; Vibrio Cholerae -- Isolation & Purification
    ISSN: 00221899
    E-ISSN: 1537-6613
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  • 4
    Language: English
    In: Journal of the Royal Society, Interface, 06 March 2015, Vol.12(104), pp.20140840
    Description: Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.
    Keywords: Ecohydrology ; Epidemic Forecast ; Model Calibration ; Model Validation ; Multilayer Network Model ; Spatially Explicit Model ; Epidemics ; Cholera -- Epidemiology
    ISSN: 17425689
    E-ISSN: 1742-5662
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  • 5
    Language: English
    In: Stochastic Environmental Research and Risk Assessment, 2016, Vol.30(8), pp.2043-2055
    Description: More than three years after its appearance in Haiti, cholera has already caused more than 8,500 deaths and 695,000 infections and it is feared to become endemic. However, no clear evidence of a stable environmental reservoir of pathogenic Vibrio cholerae , the infective agent of the disease, has emerged so far, suggesting the possibility that the transmission cycle of the disease is being maintained by bacteria freshly shed by infected individuals. Should this be the case, cholera could in principle be eradicated from Haiti. Here, we develop a framework for the estimation of the probability of extinction of the epidemic based on current information on epidemiological dynamics and health-care practice. Cholera spreading is modeled by an individual-based spatially-explicit stochastic model that accounts for the dynamics of susceptible, infected and recovered individuals hosted in different local communities connected through hydrologic and human mobility networks. Our results indicate that the probability that the epidemic goes extinct before the end of 2016 is of the order of 1 %. This low probability of extinction highlights the need for more targeted and effective interventions to possibly stop cholera in Haiti.
    Keywords: Haiti ; Cholera ; Extinction ; SIR epidemic model ; Stochastic simulator
    ISSN: 1436-3240
    E-ISSN: 1436-3259
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  • 6
    Language: English
    In: 2014, Vol.8(12), p.e3347
    Description: Despite major attempts to prevent cholera transmission, millions of people worldwide still must address this devastating disease. Cholera research has so far mainly focused on the causative agent, the bacterium Vibrio cholerae , or on disease treatment, but rarely were results from both fields interconnected. Indeed, the treatment of this severe diarrheal disease is mostly accomplished by oral rehydration therapy (ORT), whereby water and electrolytes are replenished. Commonly distributed oral rehydration salts also contain glucose. Here, we analyzed the effects of glucose and alternative carbon sources on the production of virulence determinants in the causative agent of cholera, the bacterium Vibrio cholerae during in vitro experimentation. We demonstrate that virulence gene expression and the production of cholera toxin are enhanced in the presence of glucose or similarly transported sugars in a ToxR-, TcpP- and ToxT-dependent manner. The virulence genes were significantly less expressed if alternative non-PTS carbon sources, including rice-based starch, were utilized. Notably, even though glucose-based ORT is commonly used, field studies indicated that rice-based ORT performs better. We therefore used a spatially explicit epidemiological model to demonstrate that the better performing rice-based ORT could have a significant impact on epidemic progression based on the recent outbreak of cholera in Haiti. Our results strongly support a change of carbon source for the treatment of cholera, especially in epidemic settings. ; Cholera research has so far mainly focused on the causative agent, the bacterium , or on disease treatment, but rarely were results from both fields interconnected. Indeed, the treatment of this severe diarrheal disease is mostly accomplished by oral rehydration therapy (ORT). ORT aims at rehydrating patients through the provision of water and oral rehydration salts; the latter being composed of electrolytes as well as glucose as a carbon source. Although glucose-based ORS is commonly used to treat diarrheal diseases and is recommended by the WHO, field studies on cholera indicated that rice-based ORT performs better than glucose-based ORT. Here, we investigated the impact that glucose, starch, or other carbon sources exert on . We demonstrated that glucose leads to an increased expression of the major virulence genes in the pathogen and, accordingly, to an enhanced production of cholera toxin during experimentation. Because the cholera toxin is primarily responsible for the severe symptoms that are associated with the disease, our study highlights the negative effects of glucose-based ORT. Next, we used a spatially explicit epidemiological model to demonstrate that the better performing rice-based ORS could have a significant impact on epidemic progression based on the recent outbreak of cholera in Haiti.
    Keywords: Research Article ; Biology And Life Sciences ; Medicine And Health Sciences
    ISSN: 19352727
    E-ISSN: 1935-2735
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  • 7
    In: PLoS Medicine, 2018, Vol.15(2)
    Description: Background Cholera prevention and control interventions targeted to neighbors of cholera cases (case-area targeted interventions [CATIs]), including improved water, sanitation, and hygiene, oral cholera vaccine (OCV), and prophylactic antibiotics, may be able to efficiently avert cholera cases and deaths while saving scarce resources during epidemics. Efforts to quickly target interventions to neighbors of cases have been made in recent outbreaks, but little empirical evidence related to the effectiveness, efficiency, or ideal design of this approach exists. Here, we aim to provide practical guidance on how CATIs might be used by exploring key determinants of intervention impact, including the mix of interventions, “ring” size, and timing, in simulated cholera epidemics fit to data from an urban cholera epidemic in Africa. Methods and findings We developed a micro-simulation model and calibrated it to both the epidemic curve and the small-scale spatiotemporal clustering pattern of case households from a large 2011 cholera outbreak in N’Djamena, Chad (4,352 reported cases over 232 days), and explored the potential impact of CATIs in simulated epidemics. CATIs were implemented with realistic logistical delays after cases presented for care using different combinations of prophylactic antibiotics, OCV, and/or point-of-use water treatment (POUWT) starting at different points during the epidemics and targeting rings of various radii around incident case households. Our findings suggest that CATIs shorten the duration of epidemics and are more resource-efficient than mass campaigns. OCV was predicted to be the most effective single intervention, followed by POUWT and antibiotics. CATIs with OCV started early in an epidemic focusing on a 100-m radius around case households were estimated to shorten epidemics by 68% (IQR 62% to 72%), with an 81% (IQR 69% to 87%) reduction in cases compared to uncontrolled epidemics. These same targeted interventions with OCV led to a 44-fold (IQR 27 to 78) reduction in the number of people needed to target to avert a single case of cholera, compared to mass campaigns in high-cholera-risk neighborhoods. The optimal radius to target around incident case households differed by intervention type, with antibiotics having an optimal radius of 30 m to 45 m compared to 70 m to 100 m for OCV and POUWT. Adding POUWT or antibiotics to OCV provided only marginal impact and efficiency improvements. Starting CATIs early in an epidemic with OCV and POUWT targeting those within 100 m of an incident case household reduced epidemic durations by 70% (IQR 65% to 75%) and the number of cases by 82% (IQR 71% to 88%) compared to uncontrolled epidemics. CATIs used late in epidemics, even after the peak, were estimated to avert relatively few cases but substantially reduced the number of epidemic days (e.g., by 28% [IQR 15% to 45%] for OCV in a 100-m radius). While this study is based on a rigorous, data-driven approach, the relatively high uncertainty about the ways in which POUWT and antibiotic interventions reduce cholera risk, as well as the heterogeneity in outbreak dynamics from place to place, limits the precision and generalizability of our quantitative estimates. Conclusions In this study, we found that CATIs using OCV, antibiotics, and water treatment interventions at an appropriate radius around cases could be an effective and efficient way to fight cholera epidemics. They can provide a complementary and efficient approach to mass intervention campaigns and may prove particularly useful during the initial phase of an outbreak, when there are few cases and few available resources, or in order to shorten the often protracted tails of cholera epidemics. In a modeling study, Andrew Azman and colleagues investigate the potential value of targeted interventions for control of cholera outbreaks. Author summary Why was this study done? The risk of cholera around households of cholera cases is higher than in the general population in the days after cholera symptoms start. Rapid targeting of cholera interventions to neighbors of cholera cases may provide an effective and resource-efficient way to avert cholera cases and deaths and reduce the duration of epidemics. Interventions targeted to neighbors of cases using combinations of antibiotics, oral cholera vaccine, and/or water, sanitation, and hygiene measures have been used in Africa and the Americas to fight cholera, yet limited evidence exists on the potential impact of this approach, the optimal mix of interventions, and the extent of the target population. What did the researchers do and find? Using computational models, we simulated cholera epidemics similar to a large urban cholera outbreak in Chad and evaluated the potential impact of targeted interventions administered to people living within a fixed radius (e.g., 100 m) around reported cholera cases. Targeted interventions with oral cholera vaccine were predicted to have the largest impact on reducing cases and shortening epidemics, followed by water treatment interventions and by prophylactic antibiotics, regardless of when interventions started during epidemics. The combined use of oral cholera vaccine and water treatment within 100 m around cases starting early in epidemics were estimated to lead to 70% (interquartile range [IQR] 65% to 75%) fewer epidemic days and 82% (IQR 71% to 88%) fewer cases than uncontrolled epidemics. Compared to traditional mass intervention campaigns, targeted interventions can have a similar or larger impact on epidemics and use less resources. The optimal radius to target around incident case households differed by intervention type, with antibiotics having an optimal radius of 30 m to 45 m compared to 70 m to 100 m for oral cholera vaccine and point-of-use water treatment. What do these findings mean? Interventions targeted to neighbors of cholera cases can be an effective and resource-efficient strategy to fight cholera epidemics; they may be particularly useful during the early phase of an outbreak, when the number of cases is still low, and to truncate the tails of outbreaks, after a mass intervention campaign. While field studies and/or clinical trials are needed to measure the effectiveness of targeted interventions, these results provide a rationale to focus efforts on interventions with oral cholera vaccine and water treatment interventions in a roughly 100-m radius around case households.
    Keywords: Research Article ; Medicine And Health Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Research And Analysis Methods ; Medicine And Health Sciences ; People And Places
    ISSN: 1549-1277
    E-ISSN: 1549-1676
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  • 8
    In: PLoS Computational Biology, 2018, Vol.14(5)
    Description: Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc . Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated. Author summary Mathematical models of cholera transmission can help predict the dynamics of outbreaks in near real-time in order to inform decision making for emergency management. Following the passage of Hurricane Matthew on cholera-struck Haiti in October 2016, a large oral cholera vaccine campaign targeting approximately 760,000 individuals was planned to minimize the risk of cholera transmission after the heavy hurricane rainfall. We used a reliable spatially-explicit mathematical model and state-of-the-art data assimilation techniques to predict the number of averted cases owing to the vaccination campaign. We accounted for different forecasts of precipitation patterns, a well known risk factor for the amplification of cholera epidemics, and reported near real-time projections of cholera cases for November and December 2016 to a group of epidemiologists and field researchers of Médecins Sans Frontières. Model results were then translated into operational recommendations during the outbreak management. Our projections highlighted that the departments of Grande Anse and Sud were at risk of a second epidemic wave, thus supporting the planned vaccination campaign therein. Our projections provided estimates and prediction intervals of the actual number of averted cases due to OCV per each of the 140 Haitian communes.
    Keywords: Research Article ; Medicine And Health Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Earth Sciences ; People And Places ; Biology And Life Sciences ; Biology And Life Sciences ; Social Sciences ; Biology And Life Sciences ; Earth Sciences ; Social Sciences ; Biology And Life Sciences ; Medicine And Health Sciences
    ISSN: 1553-734X
    E-ISSN: 1553-7358
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  • 9
    Language: English
    Description: Mathematical models of cholera dynamics can not only help in identifying environmental drivers and processes that influence disease transmission, but may also represent valuable tools for the prediction of the epidemiological patterns in time and space as well as for the allocation of health care resources. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. They have been ravaging the shore of Lake Kivu in the east of the country repeatedly during the last decades. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. The best fourteen models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via proper cross-validation. Among these, the one accounting for seasonality, El Niñno Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation. Some drivers (such as human mobility and rainfall) are retained only by a few models, possibly indicating that the mechanisms through which they influence cholera dynamics in the area will have to be investigated further.
    Keywords: Cholera ; Markov Chain ; Drc ; Lake Kivu ; Enso
    ISSN: 00431397
    E-ISSN: 19447973
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  • 10
    Language: English
    In: BMC Medicine, 01 March 2019, Vol.17(1), pp.1-11
    Description: Abstract Background Between August and December 2017, more than 625,000 Rohingya from Myanmar fled into Bangladesh, settling in informal makeshift camps in Cox’s Bazar district and joining 212,000 Rohingya already present. In early November, a diphtheria outbreak hit the camps, with 440 reported cases during the first month. A rise in cases during early December led to a collaboration between teams from Médecins sans Frontières—who were running a provisional diphtheria treatment centre—and the London School of Hygiene and Tropical Medicine with the goal to use transmission dynamic models to forecast the potential scale of the outbreak and the resulting resource needs. Methods We first adjusted for delays between symptom onset and case presentation using the observed distribution of reporting delays from previously reported cases. We then fit a compartmental transmission model to the adjusted incidence stratified by age group and location. Model forecasts with a lead time of 2 weeks were issued on 12, 20, 26 and 30 December and communicated to decision-makers. Results The first forecast estimated that the outbreak would peak on 19 December in Balukhali camp with 303 (95% posterior predictive interval 122–599) cases and would continue to grow in Kutupalong camp, requiring a bed capacity of 316 (95% posterior predictive interval (PPI) 197–499). On 19 December, a total of 54 cases were reported, lower than forecasted. Subsequent forecasts were more accurate: on 20 December, we predicted a total of 912 cases (95% PPI 367–2183) and 136 (95% PPI 55–327) hospitalizations until the end of the year, with 616 cases actually reported during this period. Conclusions Real-time modelling enabled feedback of key information about the potential scale of the epidemic, resource needs and mechanisms of transmission to decision-makers at a time when this information was largely unknown. By 20 December, the model generated reliable forecasts and helped support decision-making on operational aspects of the outbreak response, such as hospital bed and staff needs, and with advocacy for control measures. Although modelling is only one component of the evidence base for decision-making in outbreak situations, suitable analysis and forecasting techniques can be used to gain insights into an ongoing outbreak.
    Keywords: Diphtheria ; Real-Time Modelling ; Bangladesh ; Refugees ; Infectious Disease ; Epidemiological Modelling ; Medicine
    E-ISSN: 1741-7015
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