Evidence map›Paper›PMID 38353917›Full record

ArticleJournal of epidemiology and global health2024

Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends.

Muhammad Imran Khan, Humera Qureshi, Suk Joo Bae, Adil Shah, Naveed Ahmad, Sadique Ahmad, Muhammad Asim

Abstract read
In one paragraph

Article in Journal of epidemiology and global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Malaria Epidemiology andTropical medicine and infectious disease · 2026
    Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad Imran KhanDepartment of Industrial Engineering, Hanyang University, Seoul, South Korea.
Humera QureshiDepartment of Industrial Engineering, Hanyang University, Seoul, South Korea. humiimran1745@yahoo.com.
Suk Joo BaeDepartment of Industrial Engineering, Hanyang University, Seoul, South Korea. sjbae@hanyang.ac.kr.
Adil ShahHealth Department, Peshawar, Khyber Pakhtunkhwa, Pakistan.
Naveed AhmadEIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
Sadique AhmadEIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
Muhammad AsimEIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalaria remains a formidable worldwide health challenge, with approximately half of the global population at high risk of catching the infection. This research study aimed to address the pressing public health issue of malaria's escalating prevalence in Khyber Pakhtunkhwa (KP) province, Pakistan, and endeavors to estimate the trend for the future growth of the infection.

methodsThe data were collected from the IDSRS of KP, covering a period of 5 years from 2018 to 2022. We proposed a hybrid model that integrated Prophet and TBATS methods, allowing us to efficiently capture the complications of the malaria data and improve forecasting accuracy. To ensure an inclusive assessment, we compared the prediction performance of the proposed hybrid model with other widely used time series models, such as ARIMA, ETS, and ANN. The models were developed through R-statistical software (version 4.2.2).

resultsFor the prediction of malaria incidence, the suggested hybrid model (Prophet and TBATS) surpassed commonly used time series approaches (ARIMA, ETS, and ANN). Hybrid model assessment metrics portrayed higher accuracy and reliability with lower MAE (8913.9), RMSE (3850.2), and MAPE (0.301) values. According to our forecasts, malaria infections were predicted to spread around 99,301 by December 2023.

conclusionsWe found the hybrid model (Prophet and TBATS) outperformed common time series approaches for forecasting malaria. By December 2023, KP's malaria incidence is expected to be around 99,301, making future incidence forecasts important. Policymakers will be able to use these findings to curb disease and implement efficient policies for malaria control.

Indexed as

ForecastingMalariaHumansIncidenceModels, StatisticalPakistanInfectionKhyber PakhtunkhwaKPMalariaPakistanPrediction

Identifiers

PMID38353917
PMCPMC11043263

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.