scholarly journals Targeting hotspots to reduce transmission of malaria in Senegal: modeling of the effects of human mobility

2018 ◽  
Author(s):  
Kankoé Sallah ◽  
Roch Giorgi ◽  
El Hadj Ba ◽  
Martine Piarroux ◽  
Renaud Piarroux ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Malaria Incidence ◽  
Human Mobility ◽  
Scaling Up ◽  
Intervention Strategies ◽  
Control Measures ◽  
Transmission Season ◽  
Targeted Interventions ◽  
Additional Control ◽  
Mathematical Simulations

AbstractBackgroundIn central Senegal malaria incidences have declined in recent years in response to scaling-up of control measures, but now remains stable, making elimination improbable. Additional control measures are needed to reduce transmission.MethodsBy using a meta-population mathematical model, we evaluated chemotherapy interventions targeting stable malaria hotspots, using a differential equation framework and incorporating human mobility, and fitted to weekly malaria incidences from 45 villages, over 5 years. Three simulated approaches for selecting intervention targets were compared: a) villages with at least one malaria case during the low transmission season of the previous year; b) villages ranked highest in terms of incidence during the high transmission season of the previous year; c) villages ranked based on the degree of connectivity with adjacent populations.ResultsOur mathematical modeling, taking into account human mobility, showed that the intervention strategies targeting hotspots should be effective in reducing malaria incidence in both treated and untreated areas.ConclusionsMathematical simulations showed that targeted interventions allow increasing malaria elimination potential.

scholarly journals Targeting Malaria Hotspots to Reduce Transmission Incidence in Senegal

2020 ◽  
Author(s):  
Kankoe Sallah ◽  
Roch Giorgi ◽  
El-Hadj Ba ◽  
Martine Piarroux ◽  
Renaud Piarroux ◽  
...  
Keyword(s):  
Differential Equation ◽  
Malaria Incidence ◽  
Human Mobility ◽  
Scaling Up ◽  
Intervention Strategies ◽  
Control Measures ◽  
Transmission Season ◽  
Targeted Interventions ◽  
Additional Control ◽  
Stage 1

In central Senegal malaria incidences have declined from 2000 to 2010 in response to scaling-up of control measures and then remained stable, making elimination improbable. Additional control measures are needed to reduce transmission. We simulated chemoprophylaxis interventions targeting malaria hotspots, using a meta-population mathematical model based on differential equation framework and incorporating human mobility. The model was fitted to weekly malaria incidences from 45 villages. Three approaches for selecting intervention targets were compared: a) villages with malaria cases during the low transmission season of the previous year; b) villages with highest incidences during the high transmission season of the previous year; c) villages with highest connectivity with adjacent populations. Our modeling, considering human mobility, showed that the intervention strategies targeting hotspots would be effective in reducing malaria incidence in both targeted and untargeted areas. But whatever the intervention, pre-elimination stage (1-5 cases per 1,000 per year) would not be reached without simultaneously increasing vector control by more than 10%. Targeted interventions allow increasing overall malaria control and elimination potential.

scholarly journals Targeting Malaria Hotspots to Reduce Transmission Incidence in Senegal

2020 ◽  
Vol 18 (1) ◽  
pp. 76
Author(s):  
Kankoé Sallah ◽  
Roch Giorgi ◽  
El-Hadj Ba ◽  
Martine Piarroux ◽  
Renaud Piarroux ◽  
...  
Keyword(s):  
Malaria Incidence ◽  
Human Mobility ◽  
Intervention Strategy ◽  
Scaling Up ◽  
Intervention Strategies ◽  
Control Measures ◽  
Short Term ◽  
Transmission Season ◽  
Additional Control ◽  
The Impact

In central Senegal, malaria incidence declined in response to scaling-up of control measures from 2000 to 2010 and has since remained stable, making elimination unlikely in the short term. Additional control measures are needed to reduce transmission. We simulated chemoprophylaxis interventions targeting malaria hotspots using a metapopulation mathematical model, based on a differential-equation framework and incorporating human mobility. The model was fitted to weekly malaria incidence from 45 villages. Three approaches for selecting intervention targets were compared: (a) villages with malaria cases during the low transmission season of the previous year; (b) villages with highest incidence during the high transmission season of the previous year; (c) villages with highest connectivity with adjacent populations. Our results showed that intervention strategies targeting hotspots would be effective in reducing malaria incidence in both targeted and untargeted areas. Regardless of the intervention strategy used, pre-elimination (1–5 cases per 1000 per year) would not be reached without simultaneously increasing vector control by more than 10%. A cornerstone of malaria control and elimination is the effective targeting of strategic locations. Mathematical tools help to identify those locations and estimate the impact in silico.

scholarly journals Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Elizabeth Hyde ◽  
Matthew H. Bonds ◽  
Felana A. Ihantamalala ◽  
Ann C. Miller ◽  
Laura F. Cordier ◽  
...  
Keyword(s):  
Health Care ◽  
Access To Health Care ◽  
Malaria Incidence ◽  
Universal Access ◽  
Surveillance Systems ◽  
Passive Surveillance ◽  
Transmission Season ◽  
Access To Health ◽  
Spatio Temporal ◽  
Geographic Access To Care

Abstract Background Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care. Methods We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria. Results Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations’ financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years. Conclusions Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.

scholarly journals Stochastic and Deterministic Mathematical Modeling and Simulation to Evaluate the Novel COVID-19 Pandemic Control Measures

2020 ◽  
Vol 16 (4) ◽  
pp. 135-170
Author(s):  
Eva Gupta ◽  
Nand Jee Kanu ◽  
Amartya Munot ◽  
Venkateshwara Sutar ◽  
Umesh Kumar Vates ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Modeling And Simulation ◽  
Control Measures ◽  
The Novel

scholarly journals Mathematical Modeling of Covid-19 Disease Dynamics and Analysis of Intervention Strategies

2020 ◽  
Vol 5 (3) ◽  
pp. 176
Author(s):  
Rotich Kiplimo Titus ◽  
Lagat Robert Cheruiyot ◽  
Choge Paul Kipkurgat
Keyword(s):  
Mathematical Modeling ◽  
Intervention Strategies ◽  
Disease Dynamics

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

Risk of Exposure to Bloodborne Infection for Italian Healthcare Workers, by Job Category and Work Area

2001 ◽  
Vol 22 (4) ◽  
pp. 206-210 ◽  
Author(s):  
Vincenzo Puro ◽  
Gabriella De Carli ◽  
Nicola Petrosillo ◽  
Giuseppe Ippolito ◽  
Keyword(s):  
Infectious Disease ◽  
High Risk ◽  
Occupational Exposure ◽  
Control Measures ◽  
Full Time ◽  
Exposure Risk ◽  
Targeted Interventions ◽  
Work Area ◽  
Hiv Exposure ◽  
Job Category

AbstractObjective:To analyze the rate of occupational exposure to blood and body fluids from all sources and specifically from human immunodeficiency virus (HIV)-infected sources among hospital workers, by job category and work area.Design:Multicenter prospective study. Occupational exposure data (numerator) and full-time equivalents ([FTEs] denominator) were collected over a 5-year period (1994-1998) and analyzed.Setting:18 Italian urban acute-care hospitals with infectious disease units.Results:A total of 10,988 percutaneous and 3,361 mucocutaneous exposures were reported. The highest rate of percutaneous exposure per 100 FTEs was observed among general surgery (11%) and general medicine (10.6%) nurses, the lowest among infectious diseases (1.1%) and laboratory (1%) physicians. The highest rates of mucocutaneous exposure were observed among midwives (5.3%) and dialysis nurses (4.7%), the lowest among pathologists (0%). Inadequate sharps disposal and the prevalence of sharps in the working unit influence the risk to housekeepers. The highest combined HIV exposure rates were observed among nurses (7.8%) and physicians (1.9%) working in infectious disease units. The highest rates of high-risk percutaneous exposures per 100 FTE were again observed in nurses regardless of work area, but this risk was higher in medical areas than in surgery (odds ratio, 2.1; 95% confidence interval, 1.9-2.5; P<.0001).Conclusion:Exposure risk is related to job tasks, as well as to the type and complexity of care provided in different areas, whereas HIV exposure risk mainly relates to the prevalence of HIV-infected patients in a specific area. The number of accident-prone procedures, especially those involving the use of hollow-bore needles, performed by job category influence the rate of exposure with high risk of infection. Job- and area-specific exposure rates permit monitoring of the effectiveness of targeted interventions and control measures over time.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Simon P. Kigozi ◽  
Ruth N. Kigozi ◽  
Catherine M. Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. Methods Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. Results An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June–July experiencing highest peaks and February–March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p < 0.001) and districts Moran’s I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. Conclusion Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Mapping routine malaria incidence at village level for targeted control in Papua New Guinea

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Daniela Rodríguez-Rodríguez ◽  
Seri Maraga ◽  
Sharon Jamea-Maiasa ◽  
Anthony Tandrapah ◽  
Leo Makita ◽  
...  
Keyword(s):  
Papua New Guinea ◽  
Malaria Incidence ◽  
New Guinea ◽  
Malaria Surveillance ◽  
Targeted Interventions ◽  
Surveillance And Response ◽  
Animated Maps ◽  
Response Systems ◽  
Study Sites ◽  
Video Format

Malaria surveillance and response-systems are essential for identifying the areas most affected by malaria and for targeting interventions and optimising resources. This study aimed to assess whether the visualisation of routinely collected health facility data linked to village of residence provides evidence for targeting control interventions in four sentinel health facilities in Papua New Guinea. A video format was used to visualise the dynamics in case incidence over time and space alongside photographs illustrating the context of the data collection in the study sites. Incidence changes overtime were illustrated in animated maps. Despite limitations, this approach appeared useful in sites with very few remaining cases or with increasingly marked heterogeneity. Villages that could benefit from targeted interventions or investigations were identified.

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