Abstract

Community Health Workers (CHWs) were central figures in the public health response to COVID-19. A systematic review situates their contributions within a conceptual framework of health systems that incorporates structures, workforce capacity, and resilience. The evidence highlights the value of investing in CHW programs, strengthening their links to formal public health systems, and establishing clear training and supervision protocols. Such efforts have the potential to empower CHWs to fulfil surveillance, health education, and service-delivery roles, as well as to support the mental health and psychosocial well-being of communities during similar public health crises. Following careful consideration of the evolving situation with COVID-19, many countries have resumed—or are contemplating the resumption of—routine health services. In several contexts, however, cases remain high, and risk mitigation remains essential. Surveillance systems continue to rely on accurate community-level information about local transmission and healthcare capacity, while the reallocation of healthcare workers to other functions poses challenges for service delivery. As formal community-level surveillance and mitigation mechanisms remain constrained or unreliable, alternative approaches to engaging communities are needed. Throughout this process, the work and well-being of CHWs remain at capacity, and their mental health, welfare, and safety require oversight and support.

Keywords

  • Community Health Workers
  • Public Health Resilience
  • Pandemic Response Systems
  • Community Surveillanc

References

  1. waraka Nath Kummari,. (2022). Machine Learning Approaches to Real-Time Quality Control in Automotive Assembly Lines. Mathematical Statistician and Engineering Applications, 71(4), 16801–16820. Retrieved from https://philstat.org/index.php/MSEA/article/view/2972.
  2. Bhaumik, S., Moola, S., Tyagi, J., Nambiar, D., & Kakoti, M. (2021). Community health workers for pandemic response. BMJ Global Health, 6(1), e004195.
  3. Goutham Kumar Sheelam, "Semiconductor Innovation for Edge AI: Enabling Ultra-Low Latency in Next-Gen Wireless Networks," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2022.111258.
  4. Gilmore, B., Ndejjo, R., Tchetchia, A., de Claro, V., Mago, E., Diallo, A. A., Lopes, C., & Bhattacharyya, S. (2020). Community engagement for COVID-19 prevention. BMJ Global Health, 5(10), e003188.
  5. Meda, R. Enabling Sustainable Manufacturing Through AI-Optimized Supply Chains.
  6. Gopalan, S. S., Mohanty, S., & Das, A. (2021). Assessing community health workers’ performance during COVID-19 in India. Journal of Global Health, 11, 05018.
  7. Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. (2022). From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi.org/10.53555/sfs.v9i3.3619.
  8. Hong, H., Kim, H. J., & Lee, S. (2022). Community health workers and pandemic preparedness. International Journal of Environmental Research and Public Health, 19(3), 1234.
  9. Annapareddy, V. N. (2022). AI-Driven Optimization of Solar Power Generation Systems Through Predictive Weather and Load Modeling. Available at SSRN 5265881.
  10. Jeet, G., Thakur, J. S., Prinja, S., & Singh, M. (2021). Community health workers’ involvement in non-communicable disease services during COVID-19. BMC Health Services Research, 21, 128.
  11. Muthusamy, S., Kannan, S., Lee, M., Sanjairaj, V., Lu, W. F., Fuh, J. Y., ... & Cao, T. (2021). Cover Image, Volume 118, Number 8, August 2021. Biotechnology and Bioengineering, 118(8), i-i.
  12. Lassi, Z. S., Naseem, R., Salam, R. A., Siddiqui, F., & Bhutta, Z. A. (2021). The role of community health workers in COVID-19 response. Annals of Global Health, 87(1), 59.
  13. Sriram, H. K. (2022). Advancements in Credit Score Analytics using Deep Learning and Predictive Modeling Techniques. Available at SSRN 5255128.
  14. Mottiar, S., Lodge, M., & Byrne, C. (2021). Community health workers and trust building during COVID-19. Public Management Review, 23(11), 1672–1690.
  15. Chava, K., Chakilam, C., & Recharla, M. (2021). Machine Learning Models for Early Disease Detection: A Big Data Approach to Personalized Healthcare. International Journal of Engineering and Computer Science, 10(12), 25709–25730. https://doi.org/10.18535/ijecs.v10i12.4678.
  16. Nair, N., Tripathy, P., Sachdev, H. S., Pradhan, H., Bhattacharyya, S., Gope, R., Gagrai, S., Rath, S., Rath, S., Sinha, R., Roy, S. S., & Prost, A. (2021). Effectiveness of community mobilisation in health emergencies. BMJ Global Health, 6(3), e004131.
  17. Kommaragiri, V. B., Gadi, A. L., Kannan, S., & Preethish Nanan, B. (2021). Advanced Computational Technologies in Vehicle Production, Digital Connectivity, and Sustainable Transportation: Innovations in Intelligent Systems, Eco-Friendly Manufacturing, and Financial Optimization.
  18. Olaniran, A., Madaj, B., Bar-Zev, S., van den Broek, N., & Gupta, S. (2022). Community health workers’ mental health and wellbeing during COVID-19. PLOS Global Public Health, 2(3), e0000327.
  19. Kalisetty, S. Leveraging Cloud Computing and Big Data Analytics for Resilient Supply Chain Optimization in Retail and Manufacturing: A Framework for Disruption Management.
  20. Rahman, R., Ross, A., & Pinto, R. (2021). Addressing COVID-19 inequities through community health workers. Journal of Racial and Ethnic Health Disparities, 8(6), 1522–1529.
  21. Kothapalli Sondinti, L. R., & Syed, S. (2022). The Impact of Instant Credit Card Issuance and Personalized Financial Solutions on Enhancing Customer Experience in the Digital Banking Era. Universal Journal of Finance and Economics, 1(1), 1223. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1223.
  22. Scott, K., Beckham, S. W., Gross, M., Pariyo, G., Rao, K. D., Cometto, G., & Perry, H. B. (2021). What do we know about community health worker programs? Human Resources for Health, 19, 28.
  23. Annapareddy, V. N. (2022). Integrating AI, Machine Learning, and Cloud Computing to Drive Innovation in Renewable Energy Systems and Education Technology Solutions. Available at SSRN 5240116.
  24. Steege, R., Taegtmeyer, M., McCollum, R., Hawkins, K., Ormel, H., Kok, M., Rashid, S., Otiso, L., Sidat, M., & Theobald, S. (2021). How do gender relations affect CHWs during COVID-19? BMJ Global Health, 6(8), e005899.
  25. Varri, D. B. S. (2022). AI-Driven Risk Assessment And Compliance Automation In Multi-Cloud Environments. Journal of International Crisis and Risk Communication Research , 56–70. https://doi.org/10.63278/jicrcr.vi.3418.
  26. Tulenko, K., Vervoort, D., Craddock, S., & Serwadda, D. (2021). Health workforce response to COVID-19. PLOS Medicine, 18(12), e1003863.
  27. Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.
  28. United Nations Development Programme. (2022). Strengthening community systems for pandemic preparedness.
  29. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2022). AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents (February 07, 2022).
  30. World Health Organization. (2022). Health workforce policy and management in the context of COVID-19.
  31. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
  32. Zulu, J. M., Perry, H. B., & Scott, K. (2022). Community health workers and health system resilience. Annual Review of Public Health, 43, 55–72.