@article{Hicks:2026,
author = {Hicks, J and Munsey, A and Mousa, A and Cairns, M and Hill, A and Gowelo, S and Knock, E and Baguelin, M and Digre, P and Gogue, C and Matambisso, G and Pujol, A and Mayor, A and Wagman, J and Madanitsa, M and Khairallah, C and Kalilani-Phiri, L and Mwapasa, V and Kariuki, S and Desai, M and Maleta, K and Phiri, K and Taylor, S and Wang, D and Meshnick, S and van, Eijk A and Chandramohan, D and Kayentao, K and Williams, J and Coulibaly, S and Bojang, K and Chacky, F and Munisi, K and Aaron, S and Lazaro, S and Tagbor, H and Greenwood, B and ter, Kuile F and Fitzjohn, R and Gutman, J and Walker, P},
journal = {The Lancet Microbe},
title = {Disentangling patterns of community malaria transmission and burden using malaria prevalence among pregnant women attending antenatal care: a modelling study},
year = {2026}
}

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TY  - JOUR
AB  - Background Malaria prevalence measured among pregnant women at first antenatal care (ANC1) provides longitudinal estimates of malaria burden in pregnancy and correlates well with cross-sectional community prevalence, but additional analysis is required to estimate community incidence. We aimed to test whether ANC1-based malaria prevalence can, via an open-source, mechanistic, model-based framework, recover seasonal patterns of clinical incidence suitable for sub-regional programmatic decision-making. Methods We conducted a modelling study using monthly ANC1 malaria prevalence data from six previously published studies of malaria in pregnancy in six sub-Saharan African countries between May 2010 and August 2014. An extended, validated, age-structured malaria transmission model was fitted to monthly ANC1 malaria prevalence using particle Markov Chain Monte Carlo (pMCMC) to infer monthly clinical incidence and seasonality metrics. Agreement between model-derived incidence and independently observed time series was assessed using Markham Seasonality Index (MSI) and peak timing with concordance correlation coefficients (CCC) and 95% confidence intervals (CI). Findings Across the six ISTp datasets, total ANC1 sample sizes and positivity were: Ghana 622/1298 (47.9%); Burkina Faso 592/1413 (41.9%); Mali, 284/1308 (21.7%); The Gambia 105/1194 (8.8%); Kenya 323/1528 (21.1%); and Malawi 291/1825 (15.9%). Strong agreement was observed between model-derived incidence and independent cohort data for MSI (CCC = 0.82; 95% CI 0.31–0.97) and for peak timing (CCC = 0.98; 95% CI 0.87–0.997). Interpretation A mechanistic pMCMC framework applied to routine ANC1 data can recover clinically relevant seasonality in incidence for the broader community, enabling sub-regional timing of seasonal interventions (like seasonal malaria chemoprevention). These capabilities are especially valuable where high-quality case surveillance is limited, and household surveys are under-funded. Our
AU  - Hicks,J
AU  - Munsey,A
AU  - Mousa,A
AU  - Cairns,M
AU  - Hill,A
AU  - Gowelo,S
AU  - Knock,E
AU  - Baguelin,M
AU  - Digre,P
AU  - Gogue,C
AU  - Matambisso,G
AU  - Pujol,A
AU  - Mayor,A
AU  - Wagman,J
AU  - Madanitsa,M
AU  - Khairallah,C
AU  - Kalilani-Phiri,L
AU  - Mwapasa,V
AU  - Kariuki,S
AU  - Desai,M
AU  - Maleta,K
AU  - Phiri,K
AU  - Taylor,S
AU  - Wang,D
AU  - Meshnick,S
AU  - van,Eijk A
AU  - Chandramohan,D
AU  - Kayentao,K
AU  - Williams,J
AU  - Coulibaly,S
AU  - Bojang,K
AU  - Chacky,F
AU  - Munisi,K
AU  - Aaron,S
AU  - Lazaro,S
AU  - Tagbor,H
AU  - Greenwood,B
AU  - ter,Kuile F
AU  - Fitzjohn,R
AU  - Gutman,J
AU  - Walker,P
PY  - 2026///
SN  - 2666-5247
TI  - Disentangling patterns of community malaria transmission and burden using malaria prevalence among pregnant women attending antenatal care: a modelling study
T2  - The Lancet Microbe
ER  - 

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