Hybrid multi-disease SEIR and transformer models for infectious disease forecasting in Zambia

Authors

Mayumbo Nyirenda (ORCID: https://orcid.org/0000-0002-9615-3487)
Jackson Phiri (ORCID: https://orcid.org/0000-0002-4430-1580)
Grey Chibawe

Institutions

University of Zambia (ZM)

Publication Details

Journal: BMC Artificial Intelligence
Published: 2026-07-01
DOI: https://doi.org/10.1186/s44398-026-00031-y
Primary Topic: COVID-19 epidemiological studies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Hybrid multi-disease SEIR and transformer models for infectious disease forecasting in Zambia

Mayumbo Nyirenda, Jackson Phiri, Grey Chibawe

Jul 1, 2026

BMC Artificial Intelligence

COVID-19 epidemiological studies

article

Hybrid multi-disease SEIR and transformer models for infectious disease forecasting in Zambia

Mayumbo Nyirenda, Jackson Phiri, Grey Chibawe

July 1, 2026article en

Abstract

No abstract available for this paper.

BMC Artificial IntelligenceVol. 2(1)

University of Zambia (ZM)

Good health and well-being

Openalex Percentile: Top 8%

COVID-19 epidemiological studies

DOI Open Access PDF OpenAlex

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Hybrid multi-disease SEIR and transformer models for infectious disease forecasting in Zambia — Mayumbo Nyirenda, Jackson Phiri, et al. · BMC Artificial Intelligence (2026) | TGRS Research Map | TGRS