Malaria mortality characterization and the relationship between malaria mortality and climate in Chimoio, Mozambique

Ferrão, João Luís; Mendes, Jorge M.; Paìnho, Marco; Zacarias, Sara

doi:10.1186/s12936-017-1866-0

Cited by 12 publications

(9 citation statements)

References 15 publications

(26 reference statements)

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“…Very few ARIMA studies were carried out using weekly data. In Chimoio a similar study indicated and ARIMA (2,1,0) (2,1,1) model with the goodness of t of 72.5 % [12]. Similar studies were carried out in Ghana, Afghanistan, India, Ghana, Zambia, South Africa and Senegal and Nigeria using monthly data with comparable results [7,8,9,10,26,29].…”

Section: Discussionsupporting

confidence: 55%

“…In Africa the Box-Jenkins modelling was used in Zambia and Ghana for malaria forecasting also using monthly data [9,10]. In Mozambique malaria morbidity forecasting using ARIMA model were performed using weekly and, ARIMA intervention analysis for mortality monthly data in Chimoio Municipality [11,12]. Malaria depends largely on weather and, there are great variation over the week.…”

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confidence: 99%

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Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Ferrão

Earland

Novela³

et al. 2020

Preprint

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Malaria is one of the leading causes of morbidity and mortality in Mozambique with the 5th highest prevalence in the world. Sussundenga district in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). Sussundenga is a rural district located along the Mozambique, Zimbabwe border. P. falciparum transmission in this area is unique as there are differing control policies on each side of the international border. The study objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga district, Mozambique. Data from weekly epidemiological bulletins (BES) were collected from 2015 to 2019, which records confirmed P. falciparum cases from health facilities. These data categorize confirmed cases into two age groups: under 5 years and 5-years and older. P. falciparum incidence and temporal variation were calculated. Temporal clusters were identified using dendrograms. A time-series analysis was carried out. For temporal modeling a Box-Jenkins method was used applying an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga, 177,957 from under 5 years (47.5 %) and 194,541 (52.2 %) from 5 years and older. There were weekly and yearly variations in incidence overall (p < 0.001). There was a decreasing trend in cases for those under 5 years while there was a slight increase in those 5 and older. For those under 5, week 2 of the year had the highest number of cases (1170 Sd 34 ) while week 35 had the fewest (354 Sd 17.8). For those 5-years and older, cases also peak at week 2 (1295 Sd 245) with week 31 having the fewest cases (341 Sd 193.5). The findings indicate that cases are decreasing in those under 5 years and are increasing slightly in those 5 years and over. The P. falciparum case occurrence presents a weekly temporal pattern peaking during the wet season. Based on the temporal distribution and using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in Sussundenga District, and regionally.

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Section: Discussionsupporting

confidence: 55%

mentioning

confidence: 99%

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Ferrão

Earland

Novela³

et al. 2020

Preprint

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“… 47 Four (4%) studies excluded study regions with inconsistent datasets 8 19 67 68 while two (2%) studies used the average of its nearest values. 69 70 Other approaches used included the multivariate stepwise regression, 71 using data values extracted from previous surveys. 32 …”

Section: Resultsmentioning

confidence: 99%

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

et al. 2020

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BackgroundApproaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA).MethodsA systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion.ResultsOne hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach.ConclusionsOur review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

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“…Upon updating the number of healthy humans individuals and mosquitoes, the algorithm generates one episode of mosquito feeding in a human individual, with the possibility of protection from long lasting insecticide-impregnated nets (LLIN), insecticide-impregnated nets (ITN) or indoor residual spraying (IRS). Our model is inspired in Mozambique seasonality [28,47], considering 150 days for the duration of the high transmission season-see Figure 2. Relevant details as well as the flowchart describing the computer implementation of the agent-based model are given in Ref.…”

Section: Agent Model For Malarial Spreadingmentioning

confidence: 99%

Using the Hurst Exponent and Entropy Measures to Predict Effective Transmissibility in Empirical Series of Malaria Incidence

et al. 2022

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We analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we first derive models of the malaria incidence, the Hurst exponent and the entropy as functions of gametocytemia, measuring the infectious power of a mosquito to a human host. Second, upon estimating the values of three observables—incidence, Hurst exponent and entropy—from the data set of different malaria empirical series we predict a value of the gametocytemia for each observable. Finally, we show that the independent predictions show considerable consistency with only a few exceptions which are discussed in further detail.

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Malaria mortality characterization and the relationship between malaria mortality and climate in Chimoio, Mozambique

Cited by 12 publications

References 15 publications

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Spatial and spatio-temporal methods for mapping malaria risk: a systematic review

Using the Hurst Exponent and Entropy Measures to Predict Effective Transmissibility in Empirical Series of Malaria Incidence

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