Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
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DOI：
10.1038/s41598-020-72575-6
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发表时间：
2020-09-28
影响因子：
4.6
通讯作者：
Fernandez-Reyes D
中科院分区：
文献类型：
--
作者：
Brown BJ;Manescu P;Przybylski AA;Caccioli F;Oyinloye G;Elmi M;Shaw MJ;Pawar V;Claveau R;Shawe-Taylor J;Srinivasan MA;Afolabi NK;Rees G;Orimadegun AE;Ajetunmobi WA;Akinkunmi F;Kowobari O;Osinusi K;Akinbami FO;Omokhodion S;Shokunbi WA;Lagunju I;Sodeinde O;Fernandez-Reyes D
全球每年有超过 2 亿疟疾病例导致 50 万人死亡。由于缺乏数据、通用“单一”模型（一刀切）的趋势以及对长交付时间预测的关注，阻碍了支持疟疾护理途径的疟疾流行预测系统的开发。当前的系统无法提供适合临床实践部署的准确度的短期局部预测。在这里，我们展示了一种数据驱动的方法，该方法可以在尼日利亚一个人口稠密的常年疟疾大都市（拥有超过 350 万居民）中可靠地提前一个月进行流行率预测，尼日利亚是全球恶性疟原虫疟疾负担最严重的国家之一。我们在一项独特的 22 年前瞻性区域数据集中估计了提前一个月的患病率，该数据集包含 > 9 × 104 名参加我们医疗服务的参与者。我们的系统与验证数据的预测大小和方向一致，达到 MAE ≤ 6 × 10–2、MSE ≤ 7 × 10–3、PCC（中位数 0.63，IQR 0.3），并且超过 80% 的估计值在（+ 0.1 至 - 0.05）误差范围内，即与我们的大流行环境中的决策支持具有临床相关性。我们的数据驱动方法可以促进医疗保健系统利用自己的数据来支持当地的疟疾护理途径。
Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal “monolithic” models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 104 participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10–2, MSE ≤ 7 × 10–3, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to − 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways.
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影响因子：
6.4
作者：
Safeukui I;Gomez ND;Adelani AA;Burte F;Afolabi NK;Akondy R;Velazquez P;Holder A;Tewari R;Buffet P;Brown BJ;Shokunbi WA;Olaleye D;Sodeinde O;Kazura J;Ahmed R;Mohandas N;Fernandez-Reyes D;Haldar K
通讯作者：
Haldar K
影响因子：
3.7
作者：
Kouwaye B;Rossi F;Fonton N;Garcia A;Dossou-Gbété S;Hounkonnou MN;Cottrell G
通讯作者：
Cottrell G
影响因子：
4.6
作者：
Abah SE;Burté F;Marquet S;Brown BJ;Akinkunmi F;Oyinloye G;Afolabi NK;Omokhodion S;Lagunju I;Shokunbi WA;Wahlgren M;Dessein H;Argiro L;Dessein AJ;Noyvert B;Hunt L;Elgar G;Sodeinde O;Holder AA;Fernandez-Reyes D
通讯作者：
Fernandez-Reyes D
影响因子：
0.7
作者：
Khatri, K.;Sharma, V;Farooque, K.
通讯作者：
Farooque, K.
影响因子：
3.5
作者：
Buczak AL;Baugher B;Guven E;Ramac-Thomas LC;Elbert Y;Babin SM;Lewis SH
通讯作者：
Lewis SH