Back

Serological Markers Predict Plasmodium vivax Relapses in Returning Indonesian Soldier Cohorts

Noviyanti, R.; Setya Utami, R. A.; Smith, L.; Trianty, L.; Ekawati, L.; Sutanto, E.; Amalia, R.; Amelia, A. R.; Hafidzah, M. A.; Fadila, N.; Puspitasari, A. M.; Nisa, F. A.; Hidar, H.; Kariodimedjo, P.; Farinisia, A.; Hutahaean, G.; Christian, M.; Kesuma, T. A.; Subekti, D.; Soebianto, S.; Wulandari, F.; Nuraeni, N.; Budiman, W.; Ertanto, Y.; Widiarta, M. D.; Furkan, F.; Nekkab, N.; Mazhari, R.; White, M.; Robinson, L.; Longley, R.; Baird, J. K.; Mueller, I.

2026-06-10 infectious diseases
10.64898/2026.06.08.26355218 medRxiv
Show abstract

Summary Background Persistent transmission from relapsing Plasmodium vivax infections threatens malaria elimination programs in the Asia-Pacific and Americas. Tools to identify people at risk of relapse are urgently required. We aimed to validate a panel of eight P. vivax serological biomarkers for predicting future relapses. Methods In this observational study, soldiers returning from malaria-endemic Papua to non-endemic East Java, Indonesia, were screened at enrolment using antibody measurement (Luminex) and trained random forest classification algorithms, then followed for 6 months. Active case detection was performed fortnightly by microscopy. Algorithms classified soldiers as recently infected (last nine months) and thus at risk of relapse, based on anti-vivax antibody measurements at enrolment. Findings Between December 2018 and July 2022, 592 soldiers were enrolled, with 553 completing follow-up; 119 experienced a P. vivax relapse. Of these, 102 were correctly classified as at risk of relapse at enrolment, corresponding to 86% sensitivity and 86% specificity, with an AUC of 0.92. Interpretation P. vivax serological biomarkers can identify people at risk of relapse with high sensitivity and specificity and could be used as a novel public health intervention, P. vivax serological testing and treatment (PvSeroTAT), to reduce relapse-driven transmission.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
The Journal of Infectious Diseases
182 papers in training set
Top 0.1%
18.4%
2
BMC Medicine
163 papers in training set
Top 0.2%
10.0%
3
Clinical Infectious Diseases
231 papers in training set
Top 0.4%
10.0%
4
Malaria Journal
48 papers in training set
Top 0.3%
7.1%
5
The American Journal of Tropical Medicine and Hygiene
60 papers in training set
Top 0.5%
7.1%
50% of probability mass above
6
The Lancet Infectious Diseases
71 papers in training set
Top 0.5%
4.3%
7
The Lancet Microbe
43 papers in training set
Top 0.2%
4.3%
8
Emerging Infectious Diseases
103 papers in training set
Top 0.6%
3.6%
9
eLife
5422 papers in training set
Top 28%
3.2%
10
Scientific Reports
3102 papers in training set
Top 44%
2.7%
11
PLOS Neglected Tropical Diseases
378 papers in training set
Top 2%
2.6%
12
PLOS ONE
4510 papers in training set
Top 54%
1.7%
13
BMC Infectious Diseases
118 papers in training set
Top 3%
1.7%
14
BMJ Global Health
98 papers in training set
Top 2%
1.5%
15
eClinicalMedicine
55 papers in training set
Top 0.9%
1.3%
16
Nature Communications
4913 papers in training set
Top 56%
1.3%
17
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 4%
1.3%
18
Journal of Infection
71 papers in training set
Top 2%
1.2%
19
Transactions of The Royal Society of Tropical Medicine and Hygiene
16 papers in training set
Top 0.5%
0.9%
20
PLOS Medicine
98 papers in training set
Top 4%
0.9%
21
eBioMedicine
130 papers in training set
Top 4%
0.7%
22
Travel Medicine and Infectious Disease
15 papers in training set
Top 0.7%
0.7%
23
PLOS Global Public Health
293 papers in training set
Top 6%
0.7%
24
Open Forum Infectious Diseases
134 papers in training set
Top 3%
0.7%
25
International Journal of Infectious Diseases
126 papers in training set
Top 4%
0.6%