Five decades of infectious diseases outbreaks in the African region (1970-2018)
A geographic snapshot

doi:10.21203/rs.3.rs-2487921/v1

Download PDF

Research Article

Five decades of infectious diseases outbreaks in the African region (1970-2018) A geographic snapshot

https://doi.org/10.21203/rs.3.rs-2487921/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

This paper is being written at a time when the recent pandemic, namely COVID-19 has shaken the entire world in a manner that has never been seen in modern history. The ecology, socio-economy and weak health systems make Africa an area favorable to the occurrence of various diseases and disease outbreaks. This paper explores forty-eight (48) years of disease outbreaks in the WHO African region of the World Health Organization (WHO). Twenty-five (25) Integrated Disease Surveillance and Response priority diseases were selected, and their outbreaks described and analyzed. Using inferential spatial statistics, spatial clusters at the health district level, specifically hot spots of those outbreaks were produced and analyzed. Population at risk those hot spots were estimated. Results show a consistent report of outbreaks during the selected period with 52 outbreaks on average per year. Poliomyelitis, cholera, yellow fever, meningococcal disease and measles were the most reported epidemics. Democratic Republic of the Congo (DRC) and Nigeria were the countries reporting the highest number of outbreaks (5 on average per year) with the latter country having the highest population at risk (39M people). Despite efforts to limit their number, some disease outbreaks such as malaria, cholera, and measles continue to have a burden in terms of morbidity and mortality, while others such as poliomyelitis, yellow fever and diarrhoeal disease have shown a declining trend and the wild polio virus transmission has been eliminated in the region. Results suggest that concerted public health action may help reduce the occurrences of outbreaks in the region. Results can be used to inform preparedness and prevention activities. Priority public health actions should target DRC and Nigeria, but also identified hot spots and areas with existing risk factors within other countries.

Infectious Diseases

Diseases outbreaks

Infectious diseases

five decades

WHO African region

spatial clusters

The term epidemic refers to an increase, often sudden, in the number of cases of a disease above levels that are normally expected in a population within a defined area. The term outbreak has the same definition as epidemic but is often used for a more limited geographic area (CDC, 2020). It has long been recognized that outbreaks (and pandemics) are a threat to human populations. Africa is particularly prone to outbreaks of various types of infectious disease, that may pose multiple and complex threats, both from social, economic and sustainable development perspectives. An example is the multiple and consistent outbreaks of cholera in the region, other examples are H1N1 and COVID-19 pandemics that have considerably impacted the public health structures of countries in the continent. Ebola Virus Disease (EVD) outbreaks in West Africa and in the DRC are also important examples of outbreaks having a major impact over African societies and public health structures. Given the recurrence and impact of various outbreaks, the regional office of WHO in Africa has taken a set of initiatives for diseases outbreaks surveillance and control in the region. The third generation of the Integrated Disease Surveillance and Response (IDSR) strategy adopted by member states of the African Region focuses on integrating the guidance and requirements for collecting, analyzing, and reporting data on priority diseases at the district level (WHO 2019). It is therefore a regional effort in supporting Member States to rapidly detect public health events of international concern (PHEIC) and rapidly implement effective public health actions. IDSR goes along with global efforts to proactively develop public health systems that can prevent the spread of infectious disease and provide better containment mechanisms. Those efforts were concretely manifested through the adoption of International Health Regulations (IHR) in 2005 (WHO, 2016). The IHR is a legally binding agreement according to which WHO Member States have the obligation to report all events that may constitute PHEIC (WHO, 2016; IHR, 2005). Another WHO regional initiative is the African Public Health Emergency Fund (‎APHEF), established by the Regional Committee in 2012 with the aim of providing catalytic resources for initiating timely responses to public health emergencies (WHO, 2013). In 2017, WHO initiated the Contingency Fund for Emergencies (CFE) to ensure rapid initial response to outbreaks and other health emergencies.

In order to guide public health policies and priority actions, temporal and spatial outbreak trends of diseases in the region need to be described, analyzed and documented. Few articles have been published on historical perspectives of diseases, not necessarily diseases outbreaks in the African region of the WHO. Most of them are unique disease and country focused (Pienaar and Thompson, 2013; Sinkala et al., 2014; Pinchoff et al., 2017). Research on spatial and temporal perspective of diseases and outbreaks in that region are mostly dedicated to the Ebola Virus Disease and COVID-19 (Caroll et al., 2015; Kiskowski and Chowell, 2016; Zinszer et al., 2017; Adebayo et al., 2020;). The importance of spatial clustering of diseases and diseases related factors in diseases prevention and mitigation strategies has been shown in a study by Brownwright and colleagues (Brownwright et al, 2017). A report on epidemics risk analysis and mapping were previously performed by WHO in order to guide countries preparedness efforts (WHO 2016). An updated version of this report with bonified statistical and spatial analyses is being prepared. Talisuna and colleagues also published a paper on spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organization Africa region, focusing on the period 2016–2018. They concluded that 45% of the countries had at least one epidemic annually with the top five causes of epidemics being Cholera, Measles, Viral Haemorragic Diseases, Malaria and Meningitis (Talisuna et. al, 2020).

This paper focuses on three specific objectives: i) describing and analyzing spatial and temporal trends of 25 epidemic diseases at health district level in the countries of the WHO African region between 1970 and 2018; ii) identifying hot spots and populations at risk of these diseases at the health district level in the WHO African region over this time period; and iii) to produce statistical and geographic insights that can be used for evidence-based epidemic preparedness, public health policy and decision making in order to reduce the occurrence of outbreaks of these diseases. This may also serve as input element to better explore socio-economic and environmental risks factors related to the occurrence those diseases.

Selection of Twenty-five (25) diseases outbreaks

Diseases outbreaks reported in the WHO African region over the past forty-eight years (1970 to 2018) were reviewed. The longest period possible was considered, but there were number of limitations explaining the choice of the selected period. Most of the countries had a very embryonic health system after independences in the sixties, cases reporting was less accurate than in the most recent decades. A total of 25 priority diseases were selected with an aim to integrate the most important diseases from an IDSR perspective. All of the 25 selected diseases were officially declared and reported as outbreaks by governing authorities during the selected period.

Only laboratory confirmed outbreaks of infectious disease included in the IDSR priority diseases conditions and events guideline (IDSR, 2020) were considered in the study; no other health emergencies were included. Non-communicable and sexually transmitted diseases were also excluded. Epidemic-prone diseases were considered first, followed by diseases targeted for eradication or elimination and other major diseases as per IDSR definition (IDSR 2020). Nineteen (19) diseases were chosen based on the recurrence of their outbreaks and six (6) others were chosen primarily based on their increased risk as new disease and their potential to spread geographically given the ecology of their vectors. For example, there was only one Zika virus outbreak reported in the database, but its potential to spread spatially across the region was important given the large distribution of its vector - the Aedes mosquito.

The spatial region for this study is the 47 countries that fall under the support of the WHO Regional Office for Africa. A total of 4019 health districts were estimated for the 47 countries at the end of the study period in 2018.

Data sources

To have a rich and cross-checked epidemic database, data from various databases, reports and journal articles were assembled and made available at the health district level. The primary data source for reported outbreaks was WHO which, contributed 56.47% of the collected data. The WHO data source compiled by the WHO regional office for Africa is comprised of records from outbreaks caused by 22 epidemic and pandemic diseases that occurred in the region between 2007 and 2014. A second WHO data source was the WHO – Disease Outbreak News reports (WHO-DONs., 2019). WHO receives notifications on public health events caused by various hazardous substances from the member states as per the IHR. Events that are considered unusual and/or are associated with potential increased risks are then published on the WHO-DON website as weekly reports. All events that were declared disease outbreaks in the WHO-DON reports from 1996 to 2018 were also included in the initial database.

Additional data sources included outbreak Emergency Events Database (EM-DAT) (Guha-Sapir et al., 2019) with a contribution of 23.94% in the collected data. The EM-DAT database was launched by the Centre for Research on the Epidemiology of Disasters (CRED) to support humanitarian actions to justify decisions made during disaster preparedness, assess vulnerability to disasters and set up priority interventions.

The Program for Monitoring Emerging Diseases (ProMED-mail., 2019) contributed 15.05% of the collected data. ProMED is an open-source online reporting system established in 1994 by the Federation of American Scientists and SatelLife (SatelLife (HealthNet), 2020), to provide up-to-date information on outbreaks of diseases and exposures to toxins that affect humans as well as animals and crops grown for food. The US Center for Disease Control and Prevention website (CDC., 2019) contributed 1.40% of the collected data. From the US CDC outbreak web portal, we queried data on EVD outbreaks. The portal contained a chronological record of EVD outbreaks since 1976.

The remaining 3.14% of collected data were from various specific web sources to obtain reports on reported outbreaks

The WHO data sources were used to build the master file and then the other sources were used to update this file. The outbreaks data from each source was counter checked with data from other sources.

Demographic data were not available at the health district level for the whole region, existing one were inconsistent or obsolete for the majority of them. In order to estimate the population by district for the whole region, two main data sets were used i) A district vector layer representing the district boundaries of the 47 countries; ii) A raster layer representing a projected estimation of the total African population in 2020 per square kilometer (World pop, 2019). Using advanced geospatial algorithms, the populations were estimated and compared with the World bank population estimates at the country level (World bank, 2019).

The final database contained the following variables that were considered for analysis: Number of disease outbreaks, their location in districts and countries, the outbreak period, health district names and estimated population.

Analyses

A spatial clustering method was applied to identify spatial clusters or hot spots of epidemics. Specifically, the Getis Ord General G approach was used as a univariate model to map high and low clustering of outbreaks (Hinman et al., 2006) of all the selected disease outbreaks at the district level. Spatial clustering techniques consider proximity between geographic objects as a weight for inclusion into a cluster. They also consider the variable/variables of interest. In this study the spatial objects are the health districts of the WHO region for Africa, and the variable of interest is the number of outbreaks for the selected period (1970–2018) in each district. Geographic objects in the same cluster are similar to each other and are dissimilar to those in different clusters. Hot spots will refer to clusters with a high number of outbreaks. Getis Ord is an inferential statistic, which means that the results of the analysis are interpreted within the context of the null hypothesis. The null hypothesis for the High/Low Clustering (General G) statistic states that there is no spatial clustering of feature values. When the p-value returned by this tool is small and statistically significant, the null hypothesis can be rejected. This technique estimates density distribution of events at the local level; It allows assessment of spatial association in a study area (the WHO African Region). The maps generated show districts with highly statistically significant values of reported outbreaks at three confidence intervals: 99%, 95%, and 90%.

Populations at risk in the hot spots (areas from high clustering districts), were estimated. This was done using the previously described population data source (see the previous paragraph). In addition, the percentage of the population at risks from the hot spots was calculated using the formula below:

$${Pop}_{{prop}_{j}}=\frac{{\sum Dclpop}_{i}}{{PopCT}_{j}}$$

Where Pop _prop stands for the proportion of population in hots spots of a given country

Dclpop is a given health district population (estimated from remote sensing data)

Pop CT is the total population of a given country

i is a given district, and

j is a given country

The objective was not to integrate historical population changes, but rather to estimate the current population at risk extracted from districts that showed recurrent diseases outbreaks as illustrated by hot spots (high number outbreaks).

Descriptive charts and maps combining the 25 diseases outbreaks were developed at the district and country levels for the period 1970–2018. To consider changes in district geographies over the selected period (1970 to 2018), a method based on spatial correspondence and toponyms relationships was used. For example, if a district changed name but did not change its boundaries, the most recent name was considered. If a district changed its geography and not the name, the accuracy of events locations within most recent geography was used as a weight to attribute the event to the district.

A total of 2 370 outbreaks were reported for the selected 25 diseases and period (table 1). When outbreaks were considered per diseases, results showed that poliomyelitis outbreaks were the most frequent (31.73%), followed by cholera (20.21%), yellow fever (10.13%), meningococcal disease (7.26%), and measles (6.71%). Marburg Disease (16 outbreaks), hepatitis E (16 outbreaks), West Nile virus (4 outbreaks), and Zika virus (1 outbreak) were among the diseases having shown the smallest numbers of outbreaks (table 1).

Epidemic prone diseases
N	Selected diseases	Number of epidemics
1	Poliomyelitis	752
2	Cholera	479
3	Yellow fever	240
4	Unknown disease	115
5	Malaria	94
6	Diarrhoeal disease	60
7	Anthrax	58
8	Plague	52
9	Ebola	45
10	Lassa fever	43
11	Dengue fever	39
12	Typhoid fever	37
13	Crimean Congo	28
14	Monkey pox	27
15	Influenza	26
16	Rift Valley fever	24
17	Chikungunya	21
18	Unknown viral haemorrhagic fever	20
19	Marburg disease	16
20	West Nile fever	4
21	Unknown haemorrhagic fever	1
22	Zika virus	1
	Sub category total	2 182
Diseases targeted for eradication/elimination
1	Poliomyelitis	752
2	Meningococcal disease	172
3	Measles	159
4	Malaria	94
	Sub category total	1 177
Other major diseases
1	Hepatitis E	16
	Sub category total	16

The biggest consecutive number of outbreaks were reported from 2009 to 2011 with 24% of the reported outbreaks. The biggest yearly number of outbreaks were reported in 2009 (114 outbreaks). The lowest number of consecutive reported outbreaks was represented in the seventies. The smallest yearly number of outbreaks was reported in 1975 (2 outbreaks).

Figure 1 shows yearly trends of reported outbreaks in the WHO region from 1970 to 2018. Circles sizes reflect the number of outbreaks, while the color graduation represents the variation in the portion of outbreaks as compared to the total reported outbreaks for a specific year. The chart shows that cholera and poliomyelitis outbreaks have been consistently reported throughout the years. Cholera and poliomyelitis outbreaks also show the biggest numbers and portions over the years. However, there is an increase in the number of cholera outbreaks since 1996, while the number and proportions of poliomyelitis outbreaks have reduced since 2000. To a lesser extent, yellow fever outbreaks have also been consistently reported throughout the years. Several other disease outbreaks are consistently reported only since few years: malaria since 1986, plague since 1994, meningococcal disease since 1995, anthrax since 1999, Chikungunya since 2005. Other frequently reported outbreaks are dengue fever, diarrhoeal diseases, Ebola virus disease (EVD), and Lassa fever.

Poliomyelitis (polio) is a highly infectious viral disease that has largely affected children under 5 years of age. Reported poliomyelitis outbreaks in the WHO African Region from 1978 to 2018 showed an almost monotonically decreasing trend. However, after three years of a very low number of reported outbreaks in 2015 to 2017, a resurgence has been observed in 2018 (Fig. 2). Figure 2 shows that almost all the 47 countries of the African region have reported poliomyelitis outbreaks except Algeria and Lesotho. Nigeria and Niger have more consistently reported poliomyelitis outbreaks during this period (36 and 34 respectively). In the region, only Nigeria was classified as a poliomyelitis endemic area during the selected period of study. In August 2020, Africa was declared free from wild poliovirus (WHO, 2020), though vaccine-derived strains of polio remain causing outbreaks in recent years (WHO, 2020; Polio Global Eradication Initiative, 2020).

Cholera is an extremely virulent infectious disease caused by infection of the intestine with the toxigenic bacterium Vibrio cholerae serogroup O1 or O139. A cholera outbreak can occur in both endemic countries and in countries where cholera does not regularly occur. Reporting of cholera outbreaks in the WHO African Region from 1970 to 2018 shows an increase from 1996 to 2008 and a decrease from 2009 to 2014 (Fig. 3). From 2015 to 2018, the level of reported outbreaks has been lower and stable around 15 outbreaks per year in the region. Ghana (33 outbreaks), Uganda (25 outbreaks), Niger (23 outbreaks), Mozambique (22 outbreaks), Senegal (21 outbreaks), Nigeria (21 outbreaks), Liberia (21 outbreaks) and DRC (19 outbreaks) have more consistently reported cholera outbreaks throughout the 1970–2018 period (Fig. 3).

Yellow fever is an acute viral hemorrhagic disease caused by a flavivirus transmitted human-to-human via the domestic species of Aedes mosquitoes, or to humans from primate reservoir via a forest mosquito species (sylvatic cycle). Reported yellow fever outbreaks in the WHO African Region from 1970 to 2018 showed an increase from 2000 to 2013. Since 2014, the level of reported outbreaks has been lower and stable around four outbreaks per year in the region. Thirty (30) countries of the region reported yellow fever outbreaks during the selected period. Ghana and Nigeria have more consistently reported yellow fever outbreaks throughout the 1970–2018 period (20 outbreaks for each). Burkina Faso, Cote d’Ivoire, Liberia, Guinea, Cameroun and DRC also had an important proportion of yellow fever outbreaks in the region during that period. Burkina Faso and Ethiopia are the only countries to report two outbreaks in the same year, in 1980 for Burkina Faso and 2013 for Ethiopia (Fig. 4).

Meningococcal disease is caused by the Neisseria meningitidis bacteria. Neisseria meningitidis is found worldwide, but the highest incidence is in the “meningitis belt” of sub-Saharan Africa (WHO 2020c). Reporting of meningococcal disease outbreaks from 1970 to 2018 showed sporadic reported outbreaks from 1984 to 1995. Reported meningococcal disease outbreaks have been constant since then. An increase in the number of reported outbreaks is observed for the periods 2000 to 2002, 2007 to 2008, 2009 to 2010, and for the year 2015 (Fig. 6). Thirty-one (31) countries reported meningococcal disease outbreaks. Niger, Burkina Faso, and Benin reported the highest number of meningococcal disease outbreaks (14 for each country) closely followed by Ghana and Chad with 13 outbreaks each. Chad is the only country that reported two outbreaks during the same year (2008) (Fig. 5).

Measles is a highly contagious viral disease caused by a virus in the Paramyxovirus family and it is normally transmitted through direct contact and through the air, via droplets from the nose, mouth or throat of infected persons. It remains a major cause of death among young children globally, despite the availability of a safe and effective vaccine. Reporting of measles outbreaks in the WHO African Region from 1970 to 2018 indicates an absence from 1970 to 1979, and a sporadic occurrence from 1980 to 2004. There is an increase in the reported outbreaks from 2005 to 2011, from 2017 to 2018, and a peak in 2015. Thirty-nine (39) countries reported over 47 measles outbreaks. The exceptions were Algeria, Mauritania, Namibia, Seychelles, Cabo Verde, and Sao Tome and Principe. The highest number of measles outbreaks were in Nigeria (10 outbreaks), followed by Kenya, DRC, Cameroun (9 outbreaks for each) and then Chad and Burkina Faso (8 outbreaks for each). Central African Republic is the only country that reported two outbreaks within the same year (2009) (Fig. 6).

From 1970 to 2018, the biggest number of reported outbreaks appeared in DRC with 144 outbreaks representing 5.35% of all outbreaks in the region during the selected period. DRC was followed by Nigeria with 134 reported outbreaks (4.98% of all outbreaks), Uganda with 106 reported outbreaks (3.94% of all outbreaks), Kenya with 105 reported diseases (3.90% of all outbreaks), and Ghana with 101 reported outbreaks (3.75% of all outbreaks). Algeria (10 outbreaks), Mauritius (7 outbreaks), Seychelles (6 outbreaks), Lesotho (5 outbreaks), Equatorial Guinea (5 outbreaks) and Sao Tome and Principe (3 outbreaks) reported the lowest number of outbreaks during the selected period (Fig. 7).

Figure 8 shows the spatial clustering/hot spots of all the selected diseases outbreaks at the health district level for the period 1970–2018. Neighboring districts that reported several outbreaks during those periods are more likely to appear in the hot spots. Two hundred and fifty-two (252) districts and two hundred and nine (209) districts were identified as significant hot spots at 99% and 95% confidence intervals respectively. This makes an estimated total percentage of 11% of the districts identified as hot spots. One hundred percent (100%) of Nigerian districts reported at least one (1) disease outbreak during the 1970–2018 period, However, hot spots were located mostly in the North Eastern part of the country (Fig. 8). All Ethiopian districts reported at least one (1) disease outbreak during that same period. However, hot spots (99% CI) were located in the South Eastern and central parts of the country (Fig. 8). Central African Republic (CAR) districts also all reported outbreaks, but diseases outbreak hot spots were mostly located in a central band lying eastward - westward of the country (Fig. 9); Hot spots covering the largest spatial extent for the 1970–2018 period were in DRC with 98% of the districts affected during this period. Almost the whole country is a hot spot except the south western part (Fig. 8); Larger hot spots were also reported in Cote d’Ivoire, with 98% of districts having reported an outbreak during the 1970–2018 period. Other hot spots of a smaller spatial extent are found in northern Cameroon, Southern Chad, Southern Niger, northeast of Tanzania, Eastern of Kenya, and north of Burkina Faso (Fig. 8).

Figure 9 shows the proportion of the population at risk by country from the hot spot analysis. The percentage of Cote d’Ivoire population at risk is the highest (77.5%) followed by Central African Republic (59.0%), Ethiopia (57.8%), and Democratic Republic of the Congo (51.4%). The percentage of Nigerian population at risk (19%) although being lower than the percentage of the WHO African Region population at risk (29%), showed the most important absolute number of people that are at risk in Nigerian hot spots (over 39 million).

This analysis has also shown the consistency of the occurrence of several specific outbreaks throughout the selected period. Therefore, difficulties to tackle several of those outbreaks were also indirectly underlined. Cholera, measles, and malaria are still the main contributors to overall morbidity and mortality in the region. Other disease outbreaks such as EVD have historically had more sporadic occurrences during the 1970–2018 period, but a more drastic public health impact in shorter time (start and end of the outbreak). Considering the recent outbreaks in DRC and West Africa (2018–2021), EVD is also a good example of deadly diseases outbreaks that are becoming more frequent in the region.

Many factors can explain the presence such a high number of outbreaks occurrence and their spatial and temporal variation. This paper does not have the pretention to explore factors explaining the variety of diseases and the high number of outbreaks in the African region, however empirically, one can hypothesize that the geography in terms of climate and environment, socioeconomic determinants and the weakness of health systems in the region are important risks factors. Variations in outbreaks trends can also be explained by changes in those factors throughout the years. Variations showing a downward trend may be partly but confidently explained by public health actions at national, regional and global level. A good example is the eradication of poliomyelitis in the region.

Statistically significant hot spots of disease outbreaks suggest that outbreaks did not occur by chance but were sustained by a phenomenon or processes that in most cases are common to the identified hot spots districts. From a public health perspective, hot spots indicate those districts with highest needs and guide resource allocation. Therefore, a historical perspective (1970–2018) allows an assessment of those needs, since in general the same phenomenon or process (climate, socioeconomic determinants, health systems) would likely produce the same effect in the same conditions. Estimation of the population at risk is also an important indicator that can guide public health actions and resource allocations.

DRC and Nigeria should be targeted in priority for public health actions because outbreaks more importantly and consistently occurred in those two countries, but also because those two countries showed the biggest hot spots of outbreaks during the selected period. The identified hot spots suggest that outbreaks were/are occurring more frequently in the same areas. Those hot spots also suggest that those areas are of more concern from a public health perspective. Those two countries are also particularly of concern because of the very high number of people at risk, specifically Nigeria. Not to mention optimistic demographics projections in those countries. Uganda and Ghana have shown a high number of outbreaks but no significant hot spots. This suggests that outbreaks occurred randomly in different areas of those countries during the selected period, or surveillance systems in those countries performed better than in others. Ethiopia, Cote d'Ivoire, Cameroon, Chad, Niger, Tanzania, Kenya and Burkina Faso have showed hot spots, but lower number of outbreaks compared to DRC and Nigeria. It suggests that some outbreaks have occurred regularly in identified districts, but with less dramatic consequences on population health than in DRC and Nigeria.

Diseases for which the biggest number of outbreaks were reported have strong programs with funding. Emergence/reemergence of outbreaks for those diseases may therefore be related to complex factors as indicated earlier. Those can be more systematic in their nature, including the weakness of health systems. Specifically, public health emergency preparedness and response systems need to be strengthened. Research such as this one contributes to an evidence-based preparedness for epidemic-prone diseases. Considering the expansion of the oikumene and population movements and considering the complexity of factors that may be explaining the emergence/reemergence of infectious diseases outbreaks, it is also important to consider the effective implementation of a one health approach to balance and optimize the health of people, animals and the environment (WHO 2017).

This analysis serves to understand if/where diseases might be endemic and how to better define the endemic classifications (or epidemic thresholds) based on real data. We can check the endemicity by geo districts to look for patterns of disease shifting. Datasets and maps would likely serve as a foundation for outbreak tracking within the region, enhancing the current understanding of disease outbreaks, prevalence, and management. These analyses can also help refine the macro level health policies to achieve the strategic objectives by implementing knowledge-based specific action plans and resource allocation at local administrative/health units as indicated by the IDSR guidelines. Results could serve as a foundation to develop a long-term strategy to not only manage but also reduce the number of outbreaks in the WHO African region.

Between 1970 and 2018 2 333 outbreaks were reported for 25 selected infectious diseases including epidemic-prone, those identified for eradication and other major diseases within the African region of the WHO. Poliomyelitis, cholera, yellow fever, meningococcal diseases and measles were the most frequently reported diseases outbreaks. Reporting of many of the outbreaks showed a downward trend, with some even eradicated such as poliomyelitis. On the other hand, other outbreaks continue to have an important burden from the morbidity, mortality and public health perspectives, this is the case with cholera, malaria, measles and EVD. Specific district hot spots were identified as having been more frequently subject to the 25 selected diseases outbreaks. DRC and Nigeria are the main hot spots of those outbreaks with a more important population at risk. The example of poliomyelitis shows that despite the bio-environmental complexity and socioeconomic challenges, concerted strong public health actions can help reduce or eradicate many of those epidemics, including the most frequent and consistent such as cholera, yellow fever, and meningococcal diseases. Public health actions should target DRC and Nigeria in priority and also specifically health districts in hot spots identified by the study. At the same time prioritization of public health actions should consider countries that have weak surveillance systems.

Limitations

There are a few limitations in the data and analysis. Only reported data are analyzed and any outbreak that was not reported was not included. This is more likely to happen in the very first years of the selected period. Reporting of outbreaks may have been less efficient earlier in the period considered. Countries that have weak surveillance systems are less likely to report diseases outbreaks.

Changes in districts geographies were also a limitation. Only the most recent geographies were considered for analysis and reporting. The geography of the districts was having precedence on the toponyms. For example, if a district boundary was changed during the selected period but had kept the same name, the last geography was still considered. If the name was changed but not the geography, the last name was considered.

Disease case definitions were set by national authorities (Ministries of health/governments) so they might not be the EXACT same across the region affecting case numbers (ex. evolution of cholera distinctions in surveillance and Acute Watery Diarrheal cases).

AUTHORS CONTRIBUTION

Roland Ngom: Conceptualization, methodology, validation, Formal analysis, investigation, resources, software, data curation, visualization, writing – review and editing, supervision, project administration.

Soce Fall: Conceptualization, methodology, validation, supervision, project administration, review and editing, funding acquisition.

Oloo Stephen: Investigation, Resources, Data curation, methodology, visualization, software.

Stephanie lassieur: Investigation, Resources, Data curation, methodology, visualization, software

Shahid Rizwan: Data curation, visualization, software, review.

Valerie Mize: Review and editing.

Charles Okot: Review and editing.

Abdou Salam Gueye: Project administration, review and editing.

Waogodo Joseph Cabore: Project administration, review and editing.

COMPETING INTERESTS

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

AKNOWLEDGEMENT

The authors thank Esther Hamblion, Georges William Sie and Bedikou Diboh.

World Health Organization (2016). Mapping the Risk and Distribution of Epidemics in the WHO African Region A Technical Report. [online] Available at:

https://apps.who.int/iris/bitstream/handle/10665/206560/97892902330844.pdf?sequence=1&isAllowed=y [Accessed 12 March 2019].

World Health Organization Regional Office for Africa (2019). Technical Guidelines for Integrated Disease Surveillance and Response in the African Region: Third edition.

World Health Organization (2016). The International Health Regulation (2005). [online] Available at: https://www.who.int/ihr/publications/9789241580496/. [Accessed 12 March 2019].

World Health Organization (2005). International health regulations. Third Edition.

World Health Organization (2013). APHEF. [online] Available at: https://apps.who.int/iris/bitstream/handle/10665/94551/AFR_RC63_INF.DOC_3.pdf?sequence=1&isAllowed=y. [Accessed 4 May 2019].

Pinchoff J., Chipeta J., Banda G.C. et al. Spatial clustering of measles cases during endemic (1998–2002) and epidemic (2010) periods in Lusaka, Zambia. BMC Infect Dis 15, 121 (2015). https://doi.org/10.1186/s12879-015-0842-y.

Pienaar N.J., and Thompson P.N. (2013). Temporal and spatial history of Rift Valley fever in South Africa: 1950 to 2011. Onderstepoort Journal of Veterinary Research. Vol. 80, No. 1.

Sinkala Y., Simuunza M., Muma J.B., Pfeiffer D.U., Kasanga C.J., Mweene A. (2014). Foot and mouth disease in Zambia: spatial and temporal distributions of outbreaks, assessment of clusters and implications for control. Onderstepoort J Vet Res. 2014 Apr 23;81(2):E1-6. doi: 10.4102/ojvr.v81i2.741. PMID: 25005590.

Carroll, M., Matthews, D., Hiscox, J. et al. (2015). Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West Africa. Nature 524, 97–101 https://doi.org/10.1038/nature14594.

Kiskowski M. & Chowell G. (2016) Modeling household and community transmission of Ebola virus disease: Epidemic growth, spatial dynamics and insights for epidemic control, Virulence, 7:2, 163-173, DOI: 10.1080/21505594.2015.1076613.

Zinszer K., Morrison K., Verma A., Brownstein J.S. (2017). Spatial Determinants of Ebola Virus Disease Risk for the West African Epidemic. PLoS Curr. 2017 Mar 31;9:ecurrents.outbreaks.b494f2c6a396c72ec24cb4142765bb95. doi: 10.1371/currents.outbreaks.b494f2c6a396c72ec24cb4142765bb95. PMID: 28439448; PMCID: PMC5384853.

Adebayo A. Otitoloju, Ifeoma P. Okafor, Mayowa Fasona, Kafilat A. Bawa-Allah, Chukwuemeka Isanbor, Onyeka S. Chukwudozie, Olawale S. Folarin, Taiwo O. Adubi, Temitope O. Sogbanmu, Anthony E. Ogbeibu (2020). COVID-19 pandemic: examining the faces of spatial differences in the morbidity and mortality in sub-Saharan Africa, Europe and USA. medRxiv 2020.04.20.20072322; doi: https://doi.org/10.1101/2020.04.20.20072322.

Brownwright, T.K., Dodson, Z.M. & van Panhuis, W.G. (2017). Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa. BMC Public Health 17, 957 https://doi.org/10.1186/s12889-017-4961-9.

World Health Organization (2017). One health. [online] Available at:

https://www.who.int/news-room/questions-and-answers/item/one-health [Accessed 12 March 2019].

Talisuna, A.O., Okiro, E.A., Yahaya, A.A. et al. (2020). Spatial and temporal distribution of infectious disease epidemics, disasters and other potential public health emergencies in the World Health Organisation Africa region, 2016–2018. Global Health 16, 9 https://doi.org/10.1186/s12992-019-0540-4.

World Health Organization. (2019). Disease Outbreak News (Dons). [online] Available at: https://www.who.int/csr/don/en/. [Accessed 4 May 2019].

Guha-Sapir D, Below R, Hoyois (2019) P. EM-DAT: International Disaster Database –[online] Available at: www.emdat.be . [Accessed 18 May 2019].

ProMED-mail (2019). Promed-Mail. [online] Available at: https://promedmail.org/. [Accessed 25 March 2019].

HealthNet (2020). [online] Available at: https://www.africa.upenn.edu/E_Mail/HealthNet_SatelLife_10675.html. [Accessed 24 May 2019].

Center for Disease Control (2019). [online] Available at: https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section11.html. [Accessed 30 May 2019].

Center for Disease Control (2019). [online] Available at: www.cdc.gov/vhfebola/outbreaks/history/chronology. [Accessed 30 May 2019].

Hinman S.E., Blackburn, J.K. & Curtis (2006). A. Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi * statistic. International. Journal of Health Geography. 5, 13. https://doi.org/10.1186/1476-072X-5-13

Centers for Disease Control and Prevention (2019). Centers For Disease Control And Prevention. [online] Available at: https://www.cdc.gov/. [Accessed 16 May 2019].

Getis A., and Ord J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis 24, no. 3.

World Health Organization (2020). [online] Available at: https://www.who.int/emergencies/disease-outbreak-news/item/circulating-vaccine-derived-poliovirus-type-2-global-update. [Accessed 10 May 2019].

World Health Organization (2020). [online] Available at: https://www.who.int/news/item/25-08-2020-global-polio-eradication-initiative-applauds-who-african-region-for-wild-polio-free-certification. [Accessed 10 June 2020].

Polio Global Eradication Initiative (2020). [online] Available at: https://polioeradication.org/polio-today/polio-now/this-week/circulating-vaccine-derived-poliovirus/. [Accessed 10 June 2020].

Download PDF

Version 1

posted

You are reading this latest preprint version

Five decades of infectious diseases outbreaks in the African region (1970-2018) A geographic snapshot

Status:

Version 1

Abstract

Figures

Background

Methods

Selection of Twenty-five (25) diseases outbreaks

Data sources

Analyses

Results

Discussion

Conclusion

Limitations

Declarations

References

Status:

Version 1