Early Detection and Surveillance of Infectious Disease Outbreaks in Nigeria: Integrating Nigerian Pidgin English (Npe) Into the Epiwatch® Platform

Early Detection and Surveillance of Infectious Disease Outbreaks in Nigeria: Integrating Nigerian Pidgin English (Npe) Into the Epiwatch® Platform | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Detection and Surveillance of Infectious Disease Outbreaks in Nigeria: Integrating Nigerian Pidgin English (Npe) Into the Epiwatch® Platform Omolara Kolawole, Ashley Quigley, Abrar Chughtai, Chandini Macintyre This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5066946/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background This study aimed to integrate Nigerian Pidgin English (NPE) into the Artificial Intelligence software the EPIWATCH® with the aim of providing early detection and enhanced surveillance of infectious disease outbreaks in Nigeria. The widespread use of NPE as a national language with over 75 million speakers in Africa improves interaction and communication with communities across Nigeria. Methods For the study, key search terms were converted into NPE and then integrated into the EPIWATCH® for monitoring. A descriptive analysis was performed on publicly available data on outbreaks reported between 2018 and 2023 in Nigeria (a 5-year retrospective dataset) obtained from the EPIWATCH® to conduct descriptive analysed to compare outbreaks before and after NPE integration. Results The results showed a 166.7% increase in frequency of reported outbreaks after language integration. Conclusion This study demonstrates the potential of leveraging technology and linguistic diversity to improve disease surveillance using open source intelligence (OSINT) and response efforts in Nigeria. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In recent years, the global health community has gradually recognized the importance of the early detection of disease outbreaks together with a coordinated, public health-focused response ( 1 , 2 ). Nigeria, with its population exceeding 200 million, struggles significantly with infectious diseases ( 3 ). The country has one of the highest Tuberculosis burdens globally, whilst malaria is a major cause of illness and mortality, particularly for pregnant women and children under five. There have also been several significant infectious disease outbreaks in recent years such as the meningitis outbreak that occurred in 2017, primarily caused by Neisseria meningitidis serogroup C, which affected over 14,000 people, and resulted in more than 1,100 deaths ( 4 , 5 ). Similarly, in 2018, there was a resurgence of Lassa fever, a viral haemorrhagic fever transmitted by rodents, with over 1,000 cases reported and a high case fatality rate ( 6 ). One of the main challenges in disease response in Nigeria is the healthcare system's limited ability to quickly identify, diagnose, and contain outbreaks ( 7 ). This is reflected in the long wait times for medical care are a result of inadequate healthcare infrastructure, especially in rural areas, while vaccine hesitancy and misinformation complicate disease control efforts, particularly during immunization campaigns, shortfall in disease surveillance and reporting impede timely outbreak identification and containment ( 8 ). Despite this, Nigeria has overcome obstacles to improve laboratory diagnostics, bolster disease surveillance, and launch vaccination campaigns, all of which have increased the country's capacity to respond to diseases ( 9 , 10 ). This improvement is because of cooperation between foreign partners, healthcare professionals, and the government. In the late 20th and early 21st centuries, public health surveillance experienced significant advancements including the use of electronic health records, syndromic surveillance, big data analytics, mobile health technologies, and genomic surveillance, partly due to increased global travel and trade ( 11 , 12 ). Open-source intelligence (OSINT) has played a crucial role in this progress, providing real-time data and insights into disease outbreaks from sources like the internet and social media. The use of OSINT is valuable for disease surveillance and early response efforts, enabling authorities to promptly and efficiently identify and address outbreaks in circumstances with limited resources such as when healthcare facilities are overloaded, as was the case during the COVID-19 pandemic ( 9 , 12 , 13 ). Historically, manual reporting from medical facilities, community health workers, and laboratories has been a mainstay of grassroots disease surveillance methods. With the use of paper-based forms or phone calls to health authorities, data on disease cases, symptoms, and trends are gathered. Traditional surveillance can be slow and may miss real-time data, even though they are valuable. Conversely, surveillance systems with artificial intelligence (AI) ( 14 ), such as the EPIWATCH®, can automatically analyse massive volumes of data from multiple sources. The EPIWATCH®, which was launched in 2016, is an open-source platform monitoring epidemics by analysing social media and news for early signs of infectious diseases before official reports. The platform uses AI algorithms to scan social media posts to identify disease trends or symptoms and provide early warnings for outbreaks. By integrating AI technologies with traditional surveillance methods, the EPIWATCH® enhances disease surveillance locally, leading to improved early detection, prediction, and outbreak response ( 15 ). The West African Ebola outbreak ( 16 ), the Zika virus outbreak ( 17 ), and the polio eradication efforts ( 18 ), amongst others are instances of early detection that was made possible by the EPIWATCH®. This shows how crucial monitoring is for quickly recognizing and addressing threats to the public's health ( 14 ). Currently, health information in Nigeria is disseminated through traditional media like radio and television, as well as digital channels including the Nigerian Pidgin English (NPE) service of the BBC ( 19 , 20 ). Public health officials, healthcare facilities, and community health workers also play key roles in educating the public about health issues and promoting healthy behaviours ( 3 ). However, Nigeria's diverse linguistic landscape complicates the dissemination of health information during outbreaks due to communication barriers, resource limitations, cultural considerations, and fragmented and inconsistent health messaging that leads to confusion among the population ( 21 , 22 ). Given that rapid understanding and adherence to control measures are paramount for an effective response to outbreaks ( 13 ), language barriers can impede effective communication and coordination during public health emergencies ( 23 , 24 ). Whilst the EPIWATCH® searches 45 languages for outbreak news and significant health events as of 2023, it only includes three Nigerian languages ​​(English 7%, Arabic 1%, and French 1%) ( 22 , 25 , 26 ). This demonstrates the tool's usefulness for surveillance but also highlights its possibly limited reach in Nigeria because of language barriers ( 25 ). To address this, NPE, also known as “broken English”, is proposed as a solution due to its widespread use in Nigeria by over 75 million people, inclusivity, and cross-cultural applicability ( 3 ). NPE's versatility makes it more inclusive than indigenous languages, justifying its selection for the EPIWATCH® language integration in Nigeria ( 27 , 28 ). By integrating NPE into the EPIWATCH®, we aim to overcome language barriers and create a more inclusive and responsive system for enhanced outbreak surveillance using OSINT in Nigeria by including NPE search terms in the EPIWATCH® ( 29 ). This project seeks to incorporate Nigerian Pidgin English (NPE) into the EPIWATCH® to improve outbreak surveillance in Nigeria. The main goal is to showcase the benefits of utilizing a widely spoken local language by analysing data, focusing on linguistic accessibility, developing a framework, and measuring the impact of linguistic interventions on public health outcomes. The main objectives of this research are to conduct a descriptive analysis of infectious diseases in Nigeria over a period of five years and to integrate Nigerian Pidgin English (NPE) as a language into EPIWATCH® to enhance infectious disease surveillance in Nigeria. Methods Study design and settings The study was conducted in two phases over two study periods to allow for retrospective and prospective data review. Initially, an analysis of the frequency of outbreaks and disease trends in Nigeria over the past five years (2018–2023) was performed from data extracted from the EPIWATCH®. Subsequently, an evaluation of the efficacy of existing EPIWATCH® key terms in the NPE language for surveillance in Nigeria was conducted over a four-week period. The EPIWATCH® surveillance system is intended to track and monitor disease outbreaks through data collection from multiple sources, analysis to identify outbreaks, alert generation, response effort coordination, and monitoring and assessment of interventions' efficacy ( 30 – 32 ). In the first part of this study, a descriptive analysis of outbreaks in Nigeria over a five-year period from January 1, 2018, to December 31, 2023, was conducted using the EPIWATCH® data. The data included monthly reports of outbreaks across the country's 36 states, whilst analysis focused on variables such as outbreak frequency and geographic distribution. Ethics approval was not required as all data is open-sourced. The monthly data allowed for trend analysis and comparison with new data generated in the first part of the study. Descriptive statistics of the cleaned dataset was used to describe the outbreak trends of surveillance data from the EPIWATCH® records of all states in Nigeria over a five-year period (2018–2023). Data cleaning included dealing with missing values, removing duplicates, standardizing data formats, correcting typos, errors, or outliers that could skew the analysis, converting dates to a standard format, converting categorical data to a consistent one format, ensuring data consistency, and converting data into an appropriate format and validating data against predefined rules. Relevant data on the number of reported outbreaks and their location was retrieved, sorted, and examined for completeness. For the second part of the study, a list of key search terms was translated into NPE and integrated into the EPIWATCH®. Out of the 435 terms, 115 were translated to NPE by a native speaker and verified against current social media streams and BBC Pidgin articles ( 19 ). In NPE, many medical words are being used specifically because they are widely accepted as such. When translating the terms, care was taken to maintain meanings explicit and simplify terminology so that they can be easily understood in NPE or local context. This is especially important when describing symptoms linked to specific syndromes or diseases. As the untranslated terms are widely recognized in the medical setting, they are frequently used without a literal translation. The translation was completed within three weeks and the language integration into the EPIWATCH® quickly followed, which took two days. Prospective data collection occurred from March 5 to April 5, 2024, following the integration of NPE into the EPIWATCH®, enabled comparative analysis with the five-year retrospective surveillance data to assess the impact of NPE on surveillance in Nigeria. Specifically, common outbreaks from March 2024 were compared with those from prior years. Analysis of the regional distribution of diseases was done by studying disease patterns in different states in Nigeria. The spread of disease occurrence was assessed and reported outbreaks were mapped to visualize the distribution of diseases. Variables like environmental factors were taken into consideration to understand the geographical distribution of diseases. Results Descriptive epidemiology This analysis focuses on examining the trends of various diseases in Nigeria over the five-year period from 2018 to 2023. Overall, there were fluctuations in the burden of disease in Nigeria during the study period. As shown in Fig. 1 below, 2,688 outbreak reports were generated between January 2018 and December 2023. With 1,036 (39%) outbreak reports, 2023 recorded the most, followed by 2022 with 942 (35%) reports. In 2020, the fewest outbreaks were reported in the last four months of the year, with only 55 (2%) outbreaks. Except for 2020, which witnessed a rise in reported yellow fever outbreaks and a decrease in reported Lassa fever outbreaks, cholera and Lassa fever were the two most common diseases in reports for the years 2018, 2021, 2022, and 2023 as shown in Fig. 2 . In 2022, outbreaks of Mpox accounted for 16% of all reported outbreaks, with Lassa fever carrying the largest burden at 48%, followed by cholera at 18%. Of all the diseases reported in 2023, cholera accounted for 10%, Lassa fever for 31%, and the diphtheria outbreak for 44%. Throughout the 5-year period, there were also reports of occasional poliomyelitis and meningitis outbreaks all year round. *Lf – Lassa fever *Ch – Cholera *Yf – Yellow fever *Mg – Meningitis *Mp – Mpox *C19 – Covid19 *Me – Measles *Di – Diphtheria *Po – Poliomyelitis *Ai – Avian Influenza Apart from Edo and Ondo, which saw a steady increase in reported Lassa fever outbreaks from 2018 to 2023, the states in the Northern region of Nigeria were disproportionately affected by more diseases than any other state in the country (Fig. 3 ). Comparative Review The percentage increase in outbreak frequency after NPE integration into the EPIWATCH®, which compared common outbreaks from March 2024 with those from the same month of prior years (2018–2023) as shown in Fig. 4 , was calculated by using the median of the five previous years because the outbreak frequency data, as shown in Table 1 below, did not follow a normal distribution due to potential outliers or skewed data. Table 1 Comparison of the five top reported outbreaks in March before and after NPE integration, the EPIWATCH ® Diseases reported 2018 2019 2021 2022 2023 2024 Yellow fever 0 0 0 0 0 5 Polio 0 0 0 5 4 3 Meningitis 2 0 0 0 0 10 Diphtheria 0 0 0 0 28 12 Lassa fever 5 5 29 63 37 73 Total 9 6 42 96 72 112 The percentage increase in outbreak frequency after NPE integration in 2024, compared to the median of the previous 5 years, is approximately 166.7%, suggesting enhanced surveillance capabilities. This improvement shows how well NPE data integration works to provide timely and accurate information for disease monitoring and response in Nigeria through OSINT systems such as the EPIWATCH®. *Lf – Lassa *Yf – Yellow fever *Mg – Meningitis *Di – Diphtheria *Po - Poliomyelitis Discussion The findings in this study, demonstrate the high number of infectious diseases outbreaks in outbreaks, especially in the Northern region of the nation ( 5 , 33 ). During the five-year period, some diseases displayed an increasing trend, while others showed a declining trend. These differences could be attributed to advancements in disease surveillance, raised public awareness of health issues, and the use of focused interventions ( 6 , 10 , 34 – 36 ). Generally, outbreaks tend to peak during the dry season, correlating with increased rodent activity and food scarcity ( 37 , 38 ). As shown in Fig. 1 above, the fewest outbreaks were reported in 2020, which was probably caused by the COVID-19 era reporting gap or delayed surveillance systems that was linked to overburdened health systems in low resourced countries ( 39 , 40 ). The pattern of the trend of reported outbreaks over the five-year period was not consistent ( 41 ). Each year, more outbreaks were reported in January, September, and August than in other months, except for 2018 and 2019, when outbreaks peaked in March and November, respectively. Apart from the increased reported diphtheria outbreak in 2023 ( 42 ) and that of Mpox in 2022 ( 43 ), Lassa fever and cholera outbreaks were reported nearly year-round during the five-year period ( 44 , 45 ). Common outbreaks in Nigeria during the five-year period Lassa Fever : With outbreaks occurring frequently throughout Nigeria, Lassa fever remains a serious public health problem there. The incidence of Lassa fever cases has increased alarmingly over the past five years. Several factors contribute to the spread of the disease, such as inadequate healthcare infrastructure, poor sanitation and limited access to treatment and diagnostic services. Early detection, case management, and community-based interventions have been the mainstays of efforts to contain Lassa fever outbreaks; however, difficulties still exist in guaranteeing an efficient and prompt response to outbreaks. Cholera : Another major threat to public health in Nigeria is cholera outbreaks, which are especially common during the rainy season. Over the past five years, there has been variability in the incidence of cholera, with periodic spikes in cases reported in different states. In afflicted communities, cholera is spread by poor sanitation, tainted water sources, and overcrowding. Clean water supply, hygienic education, immunization campaigns, and better case management in medical facilities are examples of control measures. Yellow Fever : Although fewer often occurring, yellow fever outbreaks are still a concern in Nigeria. Over the previous five years, there has been fluctuation in the incidence of yellow fever, with intermittent outbreaks taking place in various areas. Initiatives to vaccinate high-risk groups have aided in slowing the disease's spread. Diphtheria : During the previous five years, there has been a decrease in the incidence of this vaccine-preventable disease in Nigeria. Diphtheria cases have decreased in part because of routine vaccination with the diphtheria-tetanus-pertussis (DTP) vaccine. To keep the spread of diphtheria under control, regular immunization programs must be strengthened, surveillance must be improved, and suspected cases must have prompt access to medical care. Mpox : Over the past five years, there have been intermittent reports of Mpox outbreaks in Nigeria, despite the disease being comparatively uncommon in comparison to other illnesses. To stop the spread of Mpox, surveillance programs and public health initiatives like case isolation and contact tracing are crucial. Early detection and response to outbreaks can be facilitated by increasing public and healthcare provider knowledge of the signs, symptoms, and mode of transmission of Mpox. The significant number of reported outbreaks recorded over the five-year study period poses a concern because many of those diseases have a high morbidity and death rate, especially when early diagnosis and proper treatment are lacking. These findings support earlier reports of endemicity in Nigeria and other West African countries ( 46 ). The substantial number of outbreaks reported in Edo and Ondo could be caused by ecological and cultural practices, or by the reservoir's existence in the states; besides having a sizable treatment facility in Edo State, where many of the country's Lassa fever cases are handled, food is also dried outside ( 38 , 47 ). It is not recommended to rely solely on prior reports of seasonal variations in case occurrence for preparatory activities, considering the nearly year-round outbreak frequency during the five-year study period and the asymmetrical pattern of peak occurrence of confirmed cases. It also supports recorded accounts that different outbreaks can happen at any time of the year. To successfully monitor and respond to disease trends and guarantee ongoing advancements in disease control and prevention initiatives, it is essential to maintain improved surveillance measures. Our analysis illustrates the geographic distribution of reported outbreaks in all the 36 states and the geopolitical zones ( 38 ). The geographical distribution pattern suggests that targeted public health interventions are needed in the northern region to mitigate the impact of outbreaks and improve overall health outcomes in these areas. A review of health patterns in Nigeria over the previous five years indicates a complicated interaction between different infectious diseases. Effective immunization campaigns have led to a decline in some diseases, like diphtheria, but the public health remains seriously threatened by others, like Lassa fever and cholera ( 6 , 48 ). The persistence of these diseases is attributed to a few factors, including limited access to healthcare services, poor sanitation, and inadequate healthcare infrastructure. Furthermore, the periodic emergence of illnesses such as Mpox and yellow fever highlights the significance of strong surveillance systems and quick reaction times in managing infectious diseases in Nigeria ( 43 , 49 ). Comprehensive strategies that combine efforts in prevention, detection, and response are necessary to effectively address the burden of these diseases ( 4 , 5 ). This entails bolstering vaccination rates, boosting access to necessary services, encouraging good sanitation and hygiene habits, and fortifying healthcare systems. To reduce the burden of infectious diseases in Nigeria and achieve long-lasting improvements in public health outcomes, cooperation between government organizations, healthcare providers, and foreign partners is essential. Also, enhanced surveillance and early detection are essential to this collaboration because they allow for the prompt identification and containment of disease outbreaks, preventing the spread of infectious diseases and lessening their effects on the populace. A major step toward enhancing disease surveillance in Nigeria is the integration of NPE into the EPIWATCH®. This approach emphasizes inclusive strategies to effectively address health challenges, while acknowledging the significance of cultural and linguistic diversity in public health interventions. By comparing the common outbreaks from March 2024 with those from previous years using the EPIWATCH®, one can gain insight into how Nigeria's disease patterns are evolving. Since certain diseases may show consistent trends over time while others may show fluctuations or emerge as new challenges, an understanding of these temporal variations is crucial for customizing public health interventions to address evolving health needs and priorities. The results hold great significance for public health response tactics in Nigeria, as improved surveillance capacities allow officials to identify epidemics sooner, take immediate action, and deploy resources more effectively. This proactive strategy is essential for preventing the spread of disease, reducing morbidity and mortality, and eventually enhancing population health outcomes. The integration of NPE into the EPIWATCH®, demonstrates the potential of using OSINT to improve disease surveillance and response. Limitations Comparing outbreaks before and after integrating NPE into the EPIWATCH® may not fully reflect the broader implications for disease surveillance and response due to limitations such as time frame i.e. one month prospective review, scope of analysis, contextual factors, etc. and methodological challenges. These drawbacks imply that the impact of NPE integration on disease surveillance and response might be more intricate and varied than a straightforward before-and-after analysis could adequately convey. While not specifically examined in this comparison, other aspects of disease surveillance and response, such as community engagement, intervention efficacy, and overall public health outcomes, may also affect the diseases trends seen in this study. Additional limitations may include the applicability of the results outside Nigeria. Conclusion Diversity in language and culture is crucial for public health interventions, as this approach emphasizes inclusive strategies to address health challenges regarding outbreak response. This study has shed light on how Nigerian disease patterns are changing by comparing outbreaks from March 2024 with those from prior years and emphasizes the need for better surveillance to enable early detection and more effective resource allocation, and to understand temporal variations for customized public health interventions. OSINT is a valuable resource which can improve surveillance and response efforts, as shown by the effective integration of NPE into the EPIWATCH®. The results highlight how important it is for NPE integration and technology developments to improve Nigeria's capacity for disease surveillance and response. Open-source early warning systems offer significant benefits compared to current methods. The EPIWATCH® offers surveillance for diseases and syndromes, enabling timely formal investigations triggered by these signals. Declarations This study is based on a comprehensive data set on infectious diseases in Nigeria over a five-year period. The data set contains information about the types of infectious diseases reported, the number of cases, the locations and dates of the reported cases, and possibly additional demographic or clinical details. In addition, data is available on the integration of Nigerian Pidgin English (NPE) into EPIWATCH®, which includes the development and implementation of the language integration as well as any relevant results or impact assessments. Ethical Approval and Consent to Participate Ethical approval was not required for this study as it did not involve human participants, personal data, or animal subjects. As such, no formal consent to participate was applicable. Conflicts of interest In this study, there is no conflict of interest to declare. Funding No funding was required or received for this study. Author Contribution O.K. wrote the main manuscript text.A.Q. and A.C. were the supervisors, who reviewed the original draft and made appropriate corrections as needed.C.M. completed the final review. Acknowledgement We would like to acknowledge the valuable contributions and support of Aye Moa for their assistance and guidance throughout the research process. Data Availability The data that support the findings of this study are available in EpiWatch at https://github.com/epiwatch/ews-dashboard. These data were derived from the following resources available in the public domain: - EPIWATCH, https://www.epiwatch.org/reports. References Houlihan CF, Whitworth JAG. Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases. Clinical Medicine [Internet]. 2019 [cited 2024 Feb 9];19:140. doi: 10.7861/CLINMEDICINE.19-2-140. Cited: in: : PMID: 30872298. 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Nwachukwu William E, Oladejo J, Ofoegbunam CM, Anueyiagu C, Dogunro F, Etiki SO, Dachung BI, Obiekea C, Aderoju B, Akanbi K, et al. Epidemiological description of and response to a large yellow fever outbreak in Edo state Nigeria, September 2018 - January 2019. BMC Public Health [Internet]. 2022 [cited 2024 Feb 11];22:1–13. doi: 10.1186/S12889-022-14043-6/TABLES/6. Cited: in: : PMID: 36042438. Silenou BC, Tom-Aba D, Adeoye O, Arinze CC, Oyiri F, Suleman AK, Yinka-Ogunleye A, Dörrbecker J, Ihekweazu C, Krause G. Use of Surveillance Outbreak Response Management and Analysis System for Human Monkeypox Outbreak, Nigeria, 2017–2019. Emerg Infect Dis [Internet]. 2020 [cited 2024 Feb 11];26:345. doi: 10.3201/EID2602.191139. Cited: in: : PMID: 31961314. Nwachukwu WE, Yusuff H, Nwangwu U, Okon A, Ogunniyi A, Imuetinyan-Clement J, Besong M, Ayo-Ajayi P, Nikau J, Baba A, et al. The response to re-emergence of yellow fever in Nigeria, 2017. Int J Infect Dis. 2020;92:189–196. doi: 10.1016/j.ijid.2019.12.034. Cited: in: : PMID: 31935537. Zhao S, Musa SS, Fu H, He D, Qin J. Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall. Epidemiol Infect [Internet]. 2020 [cited 2024 Mar 18];148. doi: 10.1017/S0950268819002267. Cited: in: : PMID: 31918780. Redding DW, Gibb R, Dan-Nwafor CC, Ilori EA, Yashe RU, Oladele SH, Amedu MO, Iniobong A, Attfield LA, Donnelly CA, et al. Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria. Nat Commun. 2021;12. doi: 10.1038/s41467-021-25910-y. Cited: in: : PMID: 34599162. Musa SS, Zhao S, Abdullahi ZU, Habib AG, He D. COVID-19 and Lassa fever in Nigeria: A deadly alliance? International Journal of Infectious Diseases [Internet]. 2022 [cited 2024 Mar 12];117:45. doi: 10.1016/J.IJID.2022.01.058. Cited: in: : PMID: 35108609. Reuben RC, Gyar SD, Makut MD, Adoga MP. Co-epidemics: have measures against COVID-19 helped to reduce Lassa fever cases in Nigeria? New Microbes New Infect [Internet]. 2021 [cited 2024 Mar 12];40. doi: 10.1016/J.NMNI.2021.100851. Cited: in: : PMID: 33614042. Omoleke SA, Ajibola O, Ajiboye JO, Raji RO. Quagmire of epidemic disease outbreaks reporting in Nigeria. BMJ Glob Health. BMJ Publishing Group; 2018. Abdulrasheed N, Lawal L, Mogaji AB, Abdulkareem AO, Shuaib AK, Adeoti SG, Amosu OP, Muhammad-Olodo AO, Lawal AO, Jaji TA, et al. Recurrent diphtheria outbreaks in Nigeria: A review of the underlying factors and remedies. Immun Inflamm Dis [Internet]. 2023 [cited 2024 Mar 18];11. doi: 10.1002/IID3.1096. Cited: in: : PMID: 38018582. Precious ND, Agboola P, Oluwatimilehin O, Olakunle OK, Olaniyi P, Adiatu AI, Olusogo AP, Obiwulu DJ, Adeola OA, Ebubechukwu ES, et al. Re-emergence of monkeypox virus outbreak in Nigeria: epidemic preparedness and response (Review-Commentary). Annals of Medicine & Surgery. 2023;85:3990–3996. doi: 10.1097/ms9.0000000000001069. Charnley GEC, Jean K, Kelman I, Gaythorpe KAM, Murray KA. Association between Conflict and Cholera in Nigeria and the Democratic Republic of the Congo. Emerg Infect Dis [Internet]. 2022 [cited 2024 Mar 18];28:2472. doi: 10.3201/EID2812.212398. Cited: in: : PMID: 36417932. Lassa Fever - Nigeria [Internet]. [cited 2024 Mar 13]. Available from: https://www.who.int/emergencies/disease-outbreak-news/item/lassa-fever---nigeria. Angell B, Sanuade O, Adetifa IMO, Okeke IN, Adamu AL, Aliyu MH, Ameh EA, Kyari F, Gadanya MA, Mabayoje DA, et al. Population health outcomes in Nigeria compared with other west African countries, 1998–2019: a systematic analysis for the Global Burden of Disease Study. The Lancet [Internet]. 2022 [cited 2024 Mar 12];399:1117–1129. doi: 10.1016/S0140-6736(21)02722-7. Cited: in: : PMID: 35303469. Prevalence of Lassa virus among rodents trapped in three Sou... : Journal of Vector Borne Diseases [Internet]. [cited 2024 Mar 18]. Available from: https://journals.lww.com/jvbd/Fulltext/2017/54020/Prevalence_of_Lassa_virus_among_rodents_trapped_in.4.aspx. Elimian KO, Mezue S, Musah A, Oyebanji O, Fall IS, Yennan S, Yao M, Abok PO, Williams N, Omar LH, et al. What are the drivers of recurrent cholera transmission in Nigeria? Evidence from a scoping review. BMC Public Health [Internet]. 2020 [cited 2024 Mar 18];20. doi: 10.1186/S12889-020-08521-Y. Cited: in: : PMID: 32245445. Opara NU, Nwagbara UI, Hlongwana KW. The COVID-19 Impact on the Trends in Yellow Fever and Lassa Fever Infections in Nigeria. Infect Dis Rep [Internet]. 2022 [cited 2024 Mar 12];14:932. doi: 10.3390/IDR14060091. Cited: in: : PMID: 36412749. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers invited by journal 27 Sep, 2024 Editor assigned by journal 20 Sep, 2024 Submission checks completed at journal 13 Sep, 2024 First submitted to journal 10 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5066946","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485459921,"identity":"1dcec2d2-5b34-4e97-98c0-f56dc00b6092","order_by":0,"name":"Omolara Kolawole","email":"","orcid":"","institution":"UNSW Sydney","correspondingAuthor":false,"prefix":"","firstName":"Omolara","middleName":"","lastName":"Kolawole","suffix":""},{"id":485459922,"identity":"0971728d-9fae-4f98-8906-2622437d1998","order_by":1,"name":"Ashley Quigley","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2Ru2rDMBRArzDEi4xXL3Z+QcZjQ77FotAu7mPK3ElTaVfnL7SVbgqCenGsjoIs7h+kW4caeu02U+2Ubhl0Fh2uOKCLAByOk4QSNZwe9LL4mc6OJnBIern4TwJDov9OwsetUu8CYln5ryiGP4U1g/1KQ1jmo0lkr/PNWkAmNb1F2fHnsmCkbDRejSdgKdOBAI5JjrLj0gbSCwS+cCKZm5rp7pB0ouHSbKXXYTKfSJgqmCZD4isUxaW6kh7BhE0kqS3Y5r6JsrWmgHKeSXuzR7mkad2OJomp0/ZjtYgfTPWGsoyleeEoZ0lSTazfQ2YRuQPKUL4HCvr/OsonYOK3vTgcDofjF18zonKUhqwTTgAAAABJRU5ErkJggg==","orcid":"","institution":"The Kirby Institute, Australia","correspondingAuthor":true,"prefix":"","firstName":"Ashley","middleName":"","lastName":"Quigley","suffix":""},{"id":485459923,"identity":"4bf06320-82cf-495b-b88e-d28d8d2ed645","order_by":2,"name":"Abrar Chughtai","email":"","orcid":"","institution":"UNSW Sydney","correspondingAuthor":false,"prefix":"","firstName":"Abrar","middleName":"","lastName":"Chughtai","suffix":""},{"id":485459924,"identity":"7b208bed-f67b-4ed7-a020-aa37184d5ff4","order_by":3,"name":"Chandini Macintyre","email":"","orcid":"","institution":"The Kirby Institute, Australia","correspondingAuthor":false,"prefix":"","firstName":"Chandini","middleName":"","lastName":"Macintyre","suffix":""}],"badges":[],"createdAt":"2024-09-10 21:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5066946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5066946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94948987,"identity":"7c49d474-3de0-4d78-b753-3bbb3410592e","added_by":"auto","created_at":"2025-11-02 11:20:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual trend of outbreaks reported in Nigeria between 2018 and 2023, the EPIWATCH\u003c/strong\u003e®\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5066946/v1/e2ce8702ef5cc1323823cec3.png"},{"id":94948984,"identity":"3fc397c3-9362-4930-97dc-d8c3166091e6","added_by":"auto","created_at":"2025-11-02 11:20:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual trend of the top 10 reported outbreaks in Nigeria over a five-year period, the EPIWATCH\u003c/strong\u003e®\u003c/p\u003e\n\u003cp\u003e*Lf – Lassa fever *Ch – Cholera *Yf – Yellow fever *Mg – Meningitis *Mp – Mpox\u003c/p\u003e\n\u003cp\u003e*C19 – Covid19 *Me – Measles *Di – Diphtheria *Po – Poliomyelitis *Ai – Avian Influenza\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5066946/v1/cca0966381e21c08b0d6e639.png"},{"id":94948985,"identity":"5e39039e-d5ec-4fda-a63f-4747f65b6799","added_by":"auto","created_at":"2025-11-02 11:20:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61010,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical distribution of the major diseases reported in Nigeria between 2018 and 2023, the EPIWATCH\u003c/strong\u003e®\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5066946/v1/f5a6dcc5aa42fd1c4e1658c4.png"},{"id":94988178,"identity":"7ccc695c-5b3b-4b4e-8858-c1f7bde16ffe","added_by":"auto","created_at":"2025-11-03 07:05:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual March outbreaks of the top five diseases reported between 2018 and 2024, the EPIWATCH\u003c/strong\u003e®\u003c/p\u003e\n\u003cp\u003e*Lf – Lassa *Yf – Yellow fever *Mg – Meningitis *Di – Diphtheria *Po - Poliomyelitis\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5066946/v1/cf2fd9b92cdfaaa644fd4022.png"},{"id":94990607,"identity":"879d84f5-82fb-40c5-8af6-4b1b6eaf44d4","added_by":"auto","created_at":"2025-11-03 07:18:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1029862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5066946/v1/d8043d0e-1165-4acf-a4b8-45fe536b03a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEarly Detection and Surveillance of Infectious Disease Outbreaks in Nigeria: Integrating Nigerian Pidgin English (Npe) Into the Epiwatch® Platform\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the global health community has gradually recognized the importance of the early detection of disease outbreaks together with a coordinated, public health-focused response (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Nigeria, with its population exceeding 200\u0026nbsp;million, struggles significantly with infectious diseases (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The country has one of the highest Tuberculosis burdens globally, whilst malaria is a major cause of illness and mortality, particularly for pregnant women and children under five. There have also been several significant infectious disease outbreaks in recent years such as the meningitis outbreak that occurred in 2017, primarily caused by \u003cem\u003eNeisseria meningitidis\u003c/em\u003e serogroup C, which affected over 14,000 people, and resulted in more than 1,100 deaths (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, in 2018, there was a resurgence of Lassa fever, a viral haemorrhagic fever transmitted by rodents, with over 1,000 cases reported and a high case fatality rate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the main challenges in disease response in Nigeria is the healthcare system's limited ability to quickly identify, diagnose, and contain outbreaks (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This is reflected in the long wait times for medical care are a result of inadequate healthcare infrastructure, especially in rural areas, while vaccine hesitancy and misinformation complicate disease control efforts, particularly during immunization campaigns, shortfall in disease surveillance and reporting impede timely outbreak identification and containment (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this, Nigeria has overcome obstacles to improve laboratory diagnostics, bolster disease surveillance, and launch vaccination campaigns, all of which have increased the country's capacity to respond to diseases (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This improvement is because of cooperation between foreign partners, healthcare professionals, and the government.\u003c/p\u003e \u003cp\u003eIn the late 20th and early 21st centuries, public health surveillance experienced significant advancements including the use of electronic health records, syndromic surveillance, big data analytics, mobile health technologies, and genomic surveillance, partly due to increased global travel and trade (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Open-source intelligence (OSINT) has played a crucial role in this progress, providing real-time data and insights into disease outbreaks from sources like the internet and social media. The use of OSINT is valuable for disease surveillance and early response efforts, enabling authorities to promptly and efficiently identify and address outbreaks in circumstances with limited resources such as when healthcare facilities are overloaded, as was the case during the COVID-19 pandemic (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Historically, manual reporting from medical facilities, community health workers, and laboratories has been a mainstay of grassroots disease surveillance methods. With the use of paper-based forms or phone calls to health authorities, data on disease cases, symptoms, and trends are gathered. Traditional surveillance can be slow and may miss real-time data, even though they are valuable. Conversely, surveillance systems with artificial intelligence (AI) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), such as the EPIWATCH\u0026reg;, can automatically analyse massive volumes of data from multiple sources. The EPIWATCH\u0026reg;, which was launched in 2016, is an open-source platform monitoring epidemics by analysing social media and news for early signs of infectious diseases before official reports. The platform uses AI algorithms to scan social media posts to identify disease trends or symptoms and provide early warnings for outbreaks. By integrating AI technologies with traditional surveillance methods, the EPIWATCH\u0026reg; enhances disease surveillance locally, leading to improved early detection, prediction, and outbreak response (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The West African Ebola outbreak (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), the Zika virus outbreak (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and the polio eradication efforts (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), amongst others are instances of early detection that was made possible by the EPIWATCH\u0026reg;. This shows how crucial monitoring is for quickly recognizing and addressing threats to the public's health (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, health information in Nigeria is disseminated through traditional media like radio and television, as well as digital channels including the Nigerian Pidgin English (NPE) service of the BBC (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Public health officials, healthcare facilities, and community health workers also play key roles in educating the public about health issues and promoting healthy behaviours (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, Nigeria's diverse linguistic landscape complicates the dissemination of health information during outbreaks due to communication barriers, resource limitations, cultural considerations, and fragmented and inconsistent health messaging that leads to confusion among the population (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Given that rapid understanding and adherence to control measures are paramount for an effective response to outbreaks (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), language barriers can impede effective communication and coordination during public health emergencies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhilst the EPIWATCH\u0026reg; searches 45 languages for outbreak news and significant health events as of 2023, it only includes three Nigerian languages ​​(English 7%, Arabic 1%, and French 1%) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This demonstrates the tool's usefulness for surveillance but also highlights its possibly limited reach in Nigeria because of language barriers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). To address this, NPE, also known as \u0026ldquo;broken English\u0026rdquo;, is proposed as a solution due to its widespread use in Nigeria by over 75\u0026nbsp;million people, inclusivity, and cross-cultural applicability (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). NPE's versatility makes it more inclusive than indigenous languages, justifying its selection for the EPIWATCH\u0026reg; language integration in Nigeria (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). By integrating NPE into the EPIWATCH\u0026reg;, we aim to overcome language barriers and create a more inclusive and responsive system for enhanced outbreak surveillance using OSINT in Nigeria by including NPE search terms in the EPIWATCH\u0026reg; (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis project seeks to incorporate Nigerian Pidgin English (NPE) into the EPIWATCH\u0026reg; to improve outbreak surveillance in Nigeria. The main goal is to showcase the benefits of utilizing a widely spoken local language by analysing data, focusing on linguistic accessibility, developing a framework, and measuring the impact of linguistic interventions on public health outcomes.\u003c/p\u003e \u003cp\u003eThe main objectives of this research are to conduct a descriptive analysis of infectious diseases in Nigeria over a period of five years and to integrate Nigerian Pidgin English (NPE) as a language into EPIWATCH\u0026reg; to enhance infectious disease surveillance in Nigeria.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and settings\u003c/h2\u003e \u003cp\u003eThe study was conducted in two phases over two study periods to allow for retrospective and prospective data review. Initially, an analysis of the frequency of outbreaks and disease trends in Nigeria over the past five years (2018\u0026ndash;2023) was performed from data extracted from the EPIWATCH\u0026reg;. Subsequently, an evaluation of the efficacy of existing EPIWATCH\u0026reg; key terms in the NPE language for surveillance in Nigeria was conducted over a four-week period. The EPIWATCH\u0026reg; surveillance system is intended to track and monitor disease outbreaks through data collection from multiple sources, analysis to identify outbreaks, alert generation, response effort coordination, and monitoring and assessment of interventions' efficacy (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the first part of this study, a descriptive analysis of outbreaks in Nigeria over a five-year period from January 1, 2018, to December 31, 2023, was conducted using the EPIWATCH\u0026reg; data. The data included monthly reports of outbreaks across the country's 36 states, whilst analysis focused on variables such as outbreak frequency and geographic distribution. Ethics approval was not required as all data is open-sourced. The monthly data allowed for trend analysis and comparison with new data generated in the first part of the study.\u003c/p\u003e \u003cp\u003eDescriptive statistics of the cleaned dataset was used to describe the outbreak trends of surveillance data from the EPIWATCH\u0026reg; records of all states in Nigeria over a five-year period (2018\u0026ndash;2023). Data cleaning included dealing with missing values, removing duplicates, standardizing data formats, correcting typos, errors, or outliers that could skew the analysis, converting dates to a standard format, converting categorical data to a consistent one format, ensuring data consistency, and converting data into an appropriate format and validating data against predefined rules. Relevant data on the number of reported outbreaks and their location was retrieved, sorted, and examined for completeness.\u003c/p\u003e \u003cp\u003eFor the second part of the study, a list of key search terms was translated into NPE and integrated into the EPIWATCH\u0026reg;. Out of the 435 terms, 115 were translated to NPE by a native speaker and verified against current social media streams and BBC Pidgin articles (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In NPE, many medical words are being used specifically because they are widely accepted as such. When translating the terms, care was taken to maintain meanings explicit and simplify terminology so that they can be easily understood in NPE or local context. This is especially important when describing symptoms linked to specific syndromes or diseases. As the untranslated terms are widely recognized in the medical setting, they are frequently used without a literal translation. The translation was completed within three weeks and the language integration into the EPIWATCH\u0026reg; quickly followed, which took two days. Prospective data collection occurred from March 5 to April 5, 2024, following the integration of NPE into the EPIWATCH\u0026reg;, enabled comparative analysis with the five-year retrospective surveillance data to assess the impact of NPE on surveillance in Nigeria. Specifically, common outbreaks from March 2024 were compared with those from prior years.\u003c/p\u003e \u003cp\u003eAnalysis of the regional distribution of diseases was done by studying disease patterns in different states in Nigeria. The spread of disease occurrence was assessed and reported outbreaks were mapped to visualize the distribution of diseases. Variables like environmental factors were taken into consideration to understand the geographical distribution of diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive epidemiology\u003c/h2\u003e \u003cp\u003eThis analysis focuses on examining the trends of various diseases in Nigeria over the five-year period from 2018 to 2023. Overall, there were fluctuations in the burden of disease in Nigeria during the study period.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below, 2,688 outbreak reports were generated between January 2018 and December 2023. With 1,036 (39%) outbreak reports, 2023 recorded the most, followed by 2022 with 942 (35%) reports. In 2020, the fewest outbreaks were reported in the last four months of the year, with only 55 (2%) outbreaks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExcept for 2020, which witnessed a rise in reported yellow fever outbreaks and a decrease in reported Lassa fever outbreaks, cholera and Lassa fever were the two most common diseases in reports for the years 2018, 2021, 2022, and 2023 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In 2022, outbreaks of Mpox accounted for 16% of all reported outbreaks, with Lassa fever carrying the largest burden at 48%, followed by cholera at 18%. Of all the diseases reported in 2023, cholera accounted for 10%, Lassa fever for 31%, and the diphtheria outbreak for 44%. Throughout the 5-year period, there were also reports of occasional poliomyelitis and meningitis outbreaks all year round.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Lf \u0026ndash; Lassa fever *Ch \u0026ndash; Cholera *Yf \u0026ndash; Yellow fever *Mg \u0026ndash; Meningitis *Mp \u0026ndash; Mpox\u003c/p\u003e \u003cp\u003e*C19 \u0026ndash; Covid19 *Me \u0026ndash; Measles *Di \u0026ndash; Diphtheria *Po \u0026ndash; Poliomyelitis *Ai \u0026ndash; Avian Influenza\u003c/p\u003e \u003cp\u003eApart from Edo and Ondo, which saw a steady increase in reported Lassa fever outbreaks from 2018 to 2023, the states in the Northern region of Nigeria were disproportionately affected by more diseases than any other state in the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eComparative Review\u003c/h2\u003e \u003cp\u003eThe percentage increase in outbreak frequency after NPE integration into the EPIWATCH\u0026reg;, which compared common outbreaks from March 2024 with those from the same month of prior years (2018\u0026ndash;2023) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, was calculated by using the median of the five previous years because the outbreak frequency data, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below, did not follow a normal distribution due to potential outliers or skewed data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of the five top reported outbreaks in March before and after NPE integration, the EPIWATCH\u003c/b\u003e\u0026reg;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases reported\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeningitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiphtheria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLassa fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe percentage increase in outbreak frequency after NPE integration in 2024, compared to the median of the previous 5 years, is approximately 166.7%, suggesting enhanced surveillance capabilities. This improvement shows how well NPE data integration works to provide timely and accurate information for disease monitoring and response in Nigeria through OSINT systems such as the EPIWATCH\u0026reg;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Lf \u0026ndash; Lassa *Yf \u0026ndash; Yellow fever *Mg \u0026ndash; Meningitis *Di \u0026ndash; Diphtheria *Po - Poliomyelitis\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings in this study, demonstrate the high number of infectious diseases outbreaks in outbreaks, especially in the Northern region of the nation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). During the five-year period, some diseases displayed an increasing trend, while others showed a declining trend. These differences could be attributed to advancements in disease surveillance, raised public awareness of health issues, and the use of focused interventions (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, outbreaks tend to peak during the dry season, correlating with increased rodent activity and food scarcity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e above, the fewest outbreaks were reported in 2020, which was probably caused by the COVID-19 era reporting gap or delayed surveillance systems that was linked to overburdened health systems in low resourced countries (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pattern of the trend of reported outbreaks over the five-year period was not consistent (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Each year, more outbreaks were reported in January, September, and August than in other months, except for 2018 and 2019, when outbreaks peaked in March and November, respectively. Apart from the increased reported diphtheria outbreak in 2023 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) and that of Mpox in 2022 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), Lassa fever and cholera outbreaks were reported nearly year-round during the five-year period (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCommon outbreaks in Nigeria during the five-year period\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLassa Fever\u003c/b\u003e: With outbreaks occurring frequently throughout Nigeria, Lassa fever remains a serious public health problem there. The incidence of Lassa fever cases has increased alarmingly over the past five years. Several factors contribute to the spread of the disease, such as inadequate healthcare infrastructure, poor sanitation and limited access to treatment and diagnostic services. Early detection, case management, and community-based interventions have been the mainstays of efforts to contain Lassa fever outbreaks; however, difficulties still exist in guaranteeing an efficient and prompt response to outbreaks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCholera\u003c/b\u003e: Another major threat to public health in Nigeria is cholera outbreaks, which are especially common during the rainy season. Over the past five years, there has been variability in the incidence of cholera, with periodic spikes in cases reported in different states. In afflicted communities, cholera is spread by poor sanitation, tainted water sources, and overcrowding. Clean water supply, hygienic education, immunization campaigns, and better case management in medical facilities are examples of control measures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eYellow Fever\u003c/b\u003e: Although fewer often occurring, yellow fever outbreaks are still a concern in Nigeria. Over the previous five years, there has been fluctuation in the incidence of yellow fever, with intermittent outbreaks taking place in various areas. Initiatives to vaccinate high-risk groups have aided in slowing the disease's spread.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiphtheria\u003c/b\u003e: During the previous five years, there has been a decrease in the incidence of this vaccine-preventable disease in Nigeria. Diphtheria cases have decreased in part because of routine vaccination with the diphtheria-tetanus-pertussis (DTP) vaccine. To keep the spread of diphtheria under control, regular immunization programs must be strengthened, surveillance must be improved, and suspected cases must have prompt access to medical care.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMpox\u003c/b\u003e: Over the past five years, there have been intermittent reports of Mpox outbreaks in Nigeria, despite the disease being comparatively uncommon in comparison to other illnesses. To stop the spread of Mpox, surveillance programs and public health initiatives like case isolation and contact tracing are crucial. Early detection and response to outbreaks can be facilitated by increasing public and healthcare provider knowledge of the signs, symptoms, and mode of transmission of Mpox.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe significant number of reported outbreaks recorded over the five-year study period poses a concern because many of those diseases have a high morbidity and death rate, especially when early diagnosis and proper treatment are lacking. These findings support earlier reports of endemicity in Nigeria and other West African countries (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The substantial number of outbreaks reported in Edo and Ondo could be caused by ecological and cultural practices, or by the reservoir's existence in the states; besides having a sizable treatment facility in Edo State, where many of the country's Lassa fever cases are handled, food is also dried outside (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). It is not recommended to rely solely on prior reports of seasonal variations in case occurrence for preparatory activities, considering the nearly year-round outbreak frequency during the five-year study period and the asymmetrical pattern of peak occurrence of confirmed cases. It also supports recorded accounts that different outbreaks can happen at any time of the year. To successfully monitor and respond to disease trends and guarantee ongoing advancements in disease control and prevention initiatives, it is essential to maintain improved surveillance measures. Our analysis illustrates the geographic distribution of reported outbreaks in all the 36 states and the geopolitical zones (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The geographical distribution pattern suggests that targeted public health interventions are needed in the northern region to mitigate the impact of outbreaks and improve overall health outcomes in these areas.\u003c/p\u003e \u003cp\u003eA review of health patterns in Nigeria over the previous five years indicates a complicated interaction between different infectious diseases. Effective immunization campaigns have led to a decline in some diseases, like diphtheria, but the public health remains seriously threatened by others, like Lassa fever and cholera (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The persistence of these diseases is attributed to a few factors, including limited access to healthcare services, poor sanitation, and inadequate healthcare infrastructure. Furthermore, the periodic emergence of illnesses such as Mpox and yellow fever highlights the significance of strong surveillance systems and quick reaction times in managing infectious diseases in Nigeria (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Comprehensive strategies that combine efforts in prevention, detection, and response are necessary to effectively address the burden of these diseases (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This entails bolstering vaccination rates, boosting access to necessary services, encouraging good sanitation and hygiene habits, and fortifying healthcare systems. To reduce the burden of infectious diseases in Nigeria and achieve long-lasting improvements in public health outcomes, cooperation between government organizations, healthcare providers, and foreign partners is essential. Also, enhanced surveillance and early detection are essential to this collaboration because they allow for the prompt identification and containment of disease outbreaks, preventing the spread of infectious diseases and lessening their effects on the populace.\u003c/p\u003e \u003cp\u003eA major step toward enhancing disease surveillance in Nigeria is the integration of NPE into the EPIWATCH\u0026reg;. This approach emphasizes inclusive strategies to effectively address health challenges, while acknowledging the significance of cultural and linguistic diversity in public health interventions. By comparing the common outbreaks from March 2024 with those from previous years using the EPIWATCH\u0026reg;, one can gain insight into how Nigeria's disease patterns are evolving. Since certain diseases may show consistent trends over time while others may show fluctuations or emerge as new challenges, an understanding of these temporal variations is crucial for customizing public health interventions to address evolving health needs and priorities. The results hold great significance for public health response tactics in Nigeria, as improved surveillance capacities allow officials to identify epidemics sooner, take immediate action, and deploy resources more effectively. This proactive strategy is essential for preventing the spread of disease, reducing morbidity and mortality, and eventually enhancing population health outcomes. The integration of NPE into the EPIWATCH\u0026reg;, demonstrates the potential of using OSINT to improve disease surveillance and response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eComparing outbreaks before and after integrating NPE into the EPIWATCH\u0026reg; may not fully reflect the broader implications for disease surveillance and response due to limitations such as time frame i.e. one month prospective review, scope of analysis, contextual factors, etc. and methodological challenges. These drawbacks imply that the impact of NPE integration on disease surveillance and response might be more intricate and varied than a straightforward before-and-after analysis could adequately convey. While not specifically examined in this comparison, other aspects of disease surveillance and response, such as community engagement, intervention efficacy, and overall public health outcomes, may also affect the diseases trends seen in this study. Additional limitations may include the applicability of the results outside Nigeria.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDiversity in language and culture is crucial for public health interventions, as this approach emphasizes inclusive strategies to address health challenges regarding outbreak response. This study has shed light on how Nigerian disease patterns are changing by comparing outbreaks from March 2024 with those from prior years and emphasizes the need for better surveillance to enable early detection and more effective resource allocation, and to understand temporal variations for customized public health interventions. OSINT is a valuable resource which can improve surveillance and response efforts, as shown by the effective integration of NPE into the EPIWATCH\u0026reg;. The results highlight how important it is for NPE integration and technology developments to improve Nigeria's capacity for disease surveillance and response. Open-source early warning systems offer significant benefits compared to current methods. The EPIWATCH\u0026reg; offers surveillance for diseases and syndromes, enabling timely formal investigations triggered by these signals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study is based on a comprehensive data set on infectious diseases in Nigeria over a five-year period. The data set contains information about the types of infectious diseases reported, the number of cases, the locations and dates of the reported cases, and possibly additional demographic or clinical details. In addition, data is available on the integration of Nigerian Pidgin English (NPE) into EPIWATCH\u0026reg;, which includes the development and implementation of the language integration as well as any relevant results or impact assessments.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eEthical approval was not required for this study as it did not involve human participants, personal data, or animal subjects. As such, no formal consent to participate was applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eIn this study, there is no conflict of interest to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was required or received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eO.K. wrote the main manuscript text.A.Q. and A.C. were the supervisors, who reviewed the original draft and made appropriate corrections as needed.C.M. completed the final review.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to acknowledge the valuable contributions and support of Aye Moa for their assistance and guidance throughout the research process.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available in EpiWatch at https://github.com/epiwatch/ews-dashboard. These data were derived from the following resources available in the public domain: - EPIWATCH, https://www.epiwatch.org/reports.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoulihan CF, Whitworth JAG. Outbreak science: recent progress in the detection and response to outbreaks of infectious diseases. Clinical Medicine [Internet]. 2019 [cited 2024 Feb 9];19:140. doi: 10.7861/CLINMEDICINE.19-2-140. Cited: in: : PMID: 30872298.\u003c/li\u003e\n\u003cli\u003eBochner AF, Makumbi I, Aderinola O, Abayneh A, Jetoh R, Yemanaberhan RL, Danjuma JS, Lazaro FT, Mahmoud HJ, Yeabah TO, et al. Implementation of the 7-1-7 target for detection, notification, and response to public health threats in five countries: a retrospective, observational study. Lancet Glob Health [Internet]. 2023 [cited 2024 Feb 9];11:e871\u0026ndash;e879. doi: 10.1016/S2214-109X(23)00133-X. Cited: in: : PMID: 37060911.\u003c/li\u003e\n\u003cli\u003eNigeria | Ethnologue Free [Internet]. 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Global Biosecurity. 2020.\u003c/li\u003e\n\u003cli\u003eAyu D, Lesmanawati S, Adam DC, Hooshmand E, Moa A, Kunasekaran MP, Chandini \u0026amp;, Macintyre R. The global epidemiology of Hepatitis A outbreaks 2016-2018 and the utility of EpiWATCH as a rapid epidemic intelligence service. Global Biosecurity. 2020.\u003c/li\u003e\n\u003cli\u003eSparks R, Jin B, Karimi S, Paris C, MacIntyre CR. Real-time monitoring of events applied to syndromic surveillance. Qual Eng. Taylor and Francis Inc.; 2019. p. 73\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eIbrahim LM, Stephen M, Okudo I, Kitgakka SM, Mamadu IN, Njai IF, Oladele S, Garba S, Ojo O, Ihekweazu C, et al. A rapid assessment of the implementation of integrated disease surveillance and response system in Northeast Nigeria, 2017. BMC Public Health [Internet]. 2020 [cited 2024 Feb 21];20:1\u0026ndash;8. doi: 10.1186/S12889-020-08707-4/TABLES/2. 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What are the drivers of recurrent cholera transmission in Nigeria? Evidence from a scoping review. BMC Public Health [Internet]. 2020 [cited 2024 Mar 18];20. doi: 10.1186/S12889-020-08521-Y. Cited: in: : PMID: 32245445.\u003c/li\u003e\n\u003cli\u003eOpara NU, Nwagbara UI, Hlongwana KW. The COVID-19 Impact on the Trends in Yellow Fever and Lassa Fever Infections in Nigeria. Infect Dis Rep [Internet]. 2022 [cited 2024 Mar 12];14:932. doi: 10.3390/IDR14060091. Cited: in: : PMID: 36412749.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5066946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5066946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to integrate Nigerian Pidgin English (NPE) into the Artificial Intelligence software the EPIWATCH\u0026reg; with the aim of providing early detection and enhanced surveillance of infectious disease outbreaks in Nigeria. The widespread use of NPE as a national language with over 75\u0026nbsp;million speakers in Africa improves interaction and communication with communities across Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFor the study, key search terms were converted into NPE and then integrated into the EPIWATCH\u0026reg; for monitoring. A descriptive analysis was performed on publicly available data on outbreaks reported between 2018 and 2023 in Nigeria (a 5-year retrospective dataset) obtained from the EPIWATCH\u0026reg; to conduct descriptive analysed to compare outbreaks before and after NPE integration.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed a 166.7% increase in frequency of reported outbreaks after language integration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates the potential of leveraging technology and linguistic diversity to improve disease surveillance using open source intelligence (OSINT) and response efforts in Nigeria.\u003c/p\u003e","manuscriptTitle":"Early Detection and Surveillance of Infectious Disease Outbreaks in Nigeria: Integrating Nigerian Pidgin English (Npe) Into the Epiwatch® Platform","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-02 11:20:18","doi":"10.21203/rs.3.rs-5066946/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-15T02:39:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T00:52:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20008025438182879596448933666969342374","date":"2025-06-12T01:36:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T04:04:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246141300652780822322973921949543705875","date":"2025-04-08T05:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242696383293904602864157191396068406185","date":"2025-04-02T20:45:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T17:40:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317743371507925850344436132649463926700","date":"2024-10-01T14:11:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-28T02:54:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-20T16:35:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T07:45:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Medical Systems","date":"2024-09-10T21:15:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-medical-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Medical Systems](https://www.springer.com/journal/10916)","snPcode":"10916","submissionUrl":"https://submission.nature.com/new-submission/10916/3","title":"Journal of Medical Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bda391e7-0b00-4e20-a0a3-b56dd84d1638","owner":[],"postedDate":"November 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T03:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-02 11:20:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5066946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5066946","identity":"rs-5066946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}