Evidence map›Paper›PMID 42462223›Full record

ArticleJournal of medical Internet research2026

Describing a National Chatbot Deployed by the Ministry of Health in Malawi During the COVID-19 Pandemic: Retrospective Data Analysis.

Isaach Ndemera, Yu-Ting Sunny Hsieh, Kingston Chirwa, Selemani Ngwira, Alanna Denny, Tsung Shu Joseph Wu, Hsin-Yi Lee, Griphin Baxter Chirambo, Simeon Luka Yosefe, Ben Chilima and 4 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Isaach NdemeraResearch Department, Luke International, Mzuzu, Malawi.ORCID https://orcid.org/0000-0003-4014-5637
Yu-Ting Sunny HsiehGraduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan.ORCID https://orcid.org/0009-0007-7748-3695
Kingston ChirwaMalawi eHealth Research Centre, Mzuzu University, Mzuzu, Malawi.ORCID https://orcid.org/0009-0002-5242-4166
Selemani NgwiraResearch Department, Luke International, Mzuzu, Malawi.ORCID https://orcid.org/0000-0001-5888-3887
Alanna DennySchool of Public Health Department of Medicine & Health, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0001-6776-0897
Tsung Shu Joseph WuResearch Department, Luke International, Mzuzu, Malawi.ORCID https://orcid.org/0000-0001-6155-9340
Hsin-Yi LeeResearch Department, Luke International, Mzuzu, Malawi.ORCID https://orcid.org/0000-0001-6309-0953
Griphin Baxter ChiramboMalawi eHealth Research Centre, Mzuzu University, Mzuzu, Malawi.ORCID https://orcid.org/0000-0001-6067-1840
Simeon Luka YosefeNational Statistics Office, Lilongwe, Malawi.ORCID https://orcid.org/0009-0000-1105-4563
Ben ChilimaPublic Health Institute of Malawi, Lilongwe, Malawi.ORCID https://orcid.org/0009-0003-1211-0768
Matthew KagoliPublic Health Institute of Malawi, Lilongwe, Malawi.ORCID https://orcid.org/0000-0002-0529-3581
Balwani MbakayaDepartment of Public Health, University of Livingstonia, Mzuzu, Malawi.ORCID https://orcid.org/0000-0002-4705-9064
Kwong Leung Joseph YuDepartment of Overseas Mission, Pingtung Christian Hospital, Pingtung, Taiwan.ORCID https://orcid.org/0009-0002-3418-3469
John O'DonoghueMalawi eHealth Research Centre, Mzuzu University, Mzuzu, Malawi.ORCID https://orcid.org/0000-0001-6056-8640

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMalawi was a pioneer among African countries in implementing a coordinated, government-led effort to streamline COVID-19 support using digital health tools. In response to the pandemic, a COVID-19 WhatsApp chatbot was developed to support the public with information, symptom reporting, and service navigation during the pandemic.

objectiveThis study describes the national deployment, functionality, and use patterns of the WhatsApp chatbot during the COVID-19 pandemic in Malawi.

methodsA retrospective descriptive analysis of chatbot interaction data from May 2020 to May 2023 was conducted. User engagement with key chatbot functions was summarized using descriptive statistics, and time-series analysis was used to compare trends in reported COVID-19 cases with access patterns to the chatbot, the emergency operation call center, and Chipatala cha pa Foni (a national hotline initiative).

resultsThe chatbot was accessed 347,117 times, with 70.8% (n=245,895) of validated WhatsApp accesses focused on COVID-19 statistics. Chatbot use patterns showed temporal alignment with COVID-19 case trends, particularly during the first and second pandemic waves and increases observed after lockdown events. Throughout the pandemic, chatbot downtime occurred in 38% (407/1070) of days, with the most prolonged period coinciding with the national vaccine rollout in 2021, during which vaccine-related functionalities were introduced. Following this expansion, the chatbot recorded 198 COVID-19 vaccine-related rumors and 644 accesses to vaccine frequently asked questions. Compared with call-based services, the chatbot recorded higher overall interactions, whereas symptoms were more frequently reported through call center platforms.

conclusionsThis study provides a descriptive account of the development and use of a national COVID-19 WhatsApp chatbot in Malawi. The findings highlight patterns of information-seeking behavior, variation in feature use, and the influence of system availability on engagement. These insights may inform the design, implementation, and sustainability of digital health communication tools in similar settings.

Indexed as

COVID-19Digital HealthHumansMalawiPandemicsRetrospective StudiesSARS-CoV-2AfricachatbotCOVID-19digital healthpandemicpreparednessresponserisk communicationsocial media

Identifiers

PMID42462223
PMCPMC13424752

What OpenQuestion holds

Textmetadata
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.