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Assessment of performance for a key indicator of One Health: evidence based on One Health index for zoonoses in Sub-Saharan Africa



Zoonoses are public health threats that cause severe damage worldwide. Zoonoses constitute a key indicator of One Health (OH) and the OH approach is being applied for zoonosis control programmes of zoonotic diseases. In a very recent study, we developed an evaluation system for OH performance through the global OH index (GOHI). This study applied the GOHI to evaluate OH performance for zoonoses in sub-Saharan Africa.


The framework for the OH index on zoonoses (OHIZ) was constructed including five indicators, 15 subindicators and 28 datasets. Publicly available data were referenced to generate the OHIZ database which included both qualitative and quantitative indicators for all sub-Sahara African countries (n = 48). The GOHI algorithm was used to estimate scores for OHIZ. Indicator weights were calculated by adopting the fuzzy analytical hierarchy process.


Overall, five indicators associated with weights were generated as follows: source of infection (23.70%), route of transmission (25.31%), targeted population (19.09%), capacity building (16.77%), and outcomes/case studies (15.13%). Following the indicators, a total of 37 sub-Sahara African countries aligned with OHIZ validation, while 11 territories were excluded for unfit or missing data. The OHIZ average score of sub-Saharan Africa was estimated at 53.67/100. The highest score was 71.99 from South Africa, while the lowest score was 40.51 from Benin. It is also worth mentioning that Sub-Sahara African countries had high performance in many subindicators associated with zoonoses, e.g., surveillance and response, vector and reservoir interventions, and natural protected areas, which suggests that this region had a certain capacity in control and prevention or responses to zoonotic events.


This study reveals that it is possible to perform OH evaluation for zoonoses in sub-Saharan Africa by OHIZ. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen strategies for effective control of zoonoses and to support the prevention of zoonotic events.


One Health (OH) is an integrated and unifying approach that aims to sustainably balance and optimize the health of people, animals and ecosystems. Hence, OH performance index is referred to as the capacity to prevent or respond to humans, animals, and the environment health threats [1]. The OH approach implies multidisciplinary efforts with common goals to achieve better public health outcomes by helping with disease prediction, prevention, and preparedness at the interface between humans, animals, and their environments [2].

In 2018, three major international organizations, the World Health Organization (WHO), the United Nations’ Food and Agriculture Organization (FAO), and the World Organization for Animal Health (OIE), put the OH vision into practice by consolidating a formal partnership and strengthening their joint action to combat human-animal-environment health risks [3]. This culminated with the FAO-OIE-WHO (tripartite) zoonoses guide, titled “Taking A Multisectoral, One Health Approach: A Tripartite Guide to Addressing Zoonotic Diseases in Countries” (2018 TZG), which provides principles and best practices to assist countries in achieving sustainable and functional collaboration at the human-animal-environment interface [4]. In May 2021, the OH High-Level Expert Panel (OHHLEP) was launched to address the emergence and spread of zoonotic diseases [5]. The panel aims to advise four international organizations—the FAO, OIE, United Nations Environment Programme (UNEP), and the WHO—on the development of a long-term global plan of action to avert outbreaks of diseases. To that end, 26 international experts have been appointed to kickstart the OHHLEP, followed by a joint tripartite (FAO, OIE, WHO) and UNEP statement that advocates for mainstream OH so that they are better prepared to prevent, predict, detect, and respond to global health threats and promote sustainable development [6, 7].

Zoonoses are infections that are naturally transmitted between human beings and other vertebrates and can spread from food, water or the environment directly. Zoonoses alone represent 60% of world known infectious diseases, with a high proportion (70%) of pathogens coming from wildlife hosts [8]. With the acceleration of globalization, zoonotic emerging and re-emerging infectious diseases seriously harm human and health, husbandry development, and food security [9]. Throughout history, several epidemics and pandemics have been associated with zoonotic origins, with rapid spatial and temporal spread worldwide. These include, but are not limited to, the bubonic plague in the fourteenth century, the 1918 influenza pandemic, acquired immune deficiency syndrome (AIDS) since 1959, severe acute respiratory syndrome (SARS) in 2003, Middle East respiratory syndrome (MERS) in 2012, and the novel coronavirus disease (COVID-19) in 2019 [10]. According to pathogen types, zoonoses are classified as bacterial zoonoses, e.g., tuberculosis and brucellosis; viral zoonoses, e.g., AIDS and rabies; helminth zoonoses, e.g., schistosomiasis and echinococcosis; protozoan zoonoses, e.g., malaria and leishmaniasis; fungous zoonoses; rickettsia zoonoses; chlamydia zoonoses; mycoplasmosis; and exceptions, such as mad cow disease [11]. Zoonoses have different ways of transmission, including animals bite or scratch, by air, aerosol or dust particles, sexual contact or mother-to-child transmission, and other ways including oral transmission, animal or environmental indirect transmission [10]. Severe zoonoses are threatening to life security, public health and economic construction globally. For example, tuberculosis, leishmaniasis, and echinococcosis are major zoonotic diseases with high prevalence and disability-adjusted life years (DALYs), which were 1,829,729,478 and 47,030,118 for tuberculosis, 4,575,092 and 696,703 for leishmaniasis, and 900,005 and 122,457 for cystic echinococcosis, respectively, according to the global burden diseases (GBD) report in 2019.

The development degree of a country or area has great significance for its governance capacity of zoonoses. Developed countries have huge advantages in medical treatment, public health, economic construction, scientific research input and social welfare that most developing settings lack [9]. Sub-Saharan Africa has long been regarded as a low-economy region with low- and middle-income countries. This would have been reflected in weak response capacity/ability to zoonotic events [12, 13]. In addition, global climate change, deforestation, and poor animal husbandry methods accelerate risks for zoonotic diseases, especially in sub-Saharan African settings [14, 15]. According to the GBD report, in 2020, sub-Saharan Africa alone recorded point prevalence and DALYs of 257,082,412 and 17,547,387, respectively, for tuberculosis and were estimated at 168 633,396 and 43,197,058, respectively, for malaria.

One Health initiative on zoonoses, including governance capacity in surveillance and research activities, has been carried out in many countries/territories across the African continent [16]. In Kenya and Uganda, a global disease detection division [17] and a mutidisciplinary platform [18], respectively, have been established for zoonoses control and prevention under the OH approach. In the Horn of Africa (Ethiopia, Kenya, Uganda, Sudan, South Sudan, Somalia, Djibouti and Eritrea) and in Chad, Côte d'Ivoire, and Mali, international cooperation on the OH approach has been established for capacity building to support zoonoses control and prevention [19, 20]. However, such activities lack efficient interdepartmental collaboration mechanisms, or few outcomes are adequate to be implemented in local communities [18, 21].

In this study, we formulated indicators for zoonoses and applied OH principles [22, 23] to data retrieved from publicly available repositories to systematically analyze the OH index for zoonoses in sub-Saharan Africa. In addition, five major zoonotic diseases of public health importance worldwide such as tuberculosis, COVID-19, echinococcosis, leishmaniasis, and rabies [24], were selected as case studies for assessment. Findings from these aforementioned studies suggest imperative needs of the OH approach not only to consolidate existing achievements but also to implement integrative strategies in the control programmes of zoonotic events in sub-Saharan Africa.


OH principles were applied to evaluate OH performance for zoonoses in sub-Saharan Africa. This study defines the OH index on zoonoses (OHIZ) as an indicator to assess the capacity of a country/territory to respond to or prevent zoonotic events associated with the holistic health of the human-animal-environmental interface. Figure 1 shows the schematic of the construction steps of OH performance assessment for zoonoses, which included formulation of OHIZ, selection of indicators, database building, and OHIZ calculation.

Fig. 1
figure 1

Flow chart for the processes involved in the assessment of the One Health performance for zoonoses. OHIZ One Health index on zoonoses, OHi One Health index

Formulation of OHIZ and selection of indicators associated with zoonoses

Selection of the OHIZ database for zoonoses was based on seven principles as reported by Zhang and colleagues [23]. These data met the following selection criteria: fit to corresponding indicators of zoonoses; originated from authoritative sources with global or local zoonotic data; is available from public open sources with clear method of collecting; cover a sufficient number of countries/territories; cover recent temporal period and are updated annually; are measured with an established and unified method and peer-reviewed across countries/territories for single indicators; describe the status of zoonoses in the indicators at country-level.

Accordingly, three elements (indicators) of infectious diseases were included in the OHIZ framework and were referred to as the source of infection, route of transmission, and targeted population. Given that the OH approach covers areas of policy support, scientific research, and infrastructure construction, an indicator termed capacity building was also set. A further indicator referred to as outcomes (case studies) was added to form a five-indicator panel for OHIZ. Indicators that did not meet the OHIZ principles were excluded. Subindicators were conceived following the abovementioned indicators and information from previous studies [10, 11, 25, 26]. Zoonotic diseases of public health importance, e.g., tuberculosis, echinococcosis, leishmaniasis, and rabies were selected and were associated with the outcome indicator as cases studies. In addition, COVID-19, a newly emerging zoonotic disease of likely bat origin that has a huge impact on humanity and the environment potentially [9, 10], were also selected. The panel of indicators framework was thereafter developed into a set of 15 subindicators (Table 1).

Table 1 List of all-level OHIZ indicators and datasets alongside their weights

Building of the OHIZ database

The panel of indicators was classified into qualitative and quantitative indicators according to the database sets that were consulted. Data collection was applied to qualitative indicators, while data were retrieved for quantitative indicators [22, 23, 27]. During OHIZ data collection, qualitative data were labeled “0” for “data not found” and “1” for “data found”.

OHIZ database building referred to internationally published authoritative databases. A total of 28 comprehensive sets of OHIZ data were identified, including 13 datasets retrieved from the WHO database [28], three datasets from the OIE-WHAIS and the FAO-Emergency Prevention System for Animal Health (EMPRES) database [29], four datasets from the World Bank (WB) database [30], four from the global health security (GHS) index [31], and four from the GBD database of global health data exchange (GHDx) [32, 33]. Details on the data sources are listed in Additional file 1. After the OHIZ database was generated, all data were checked for consistency and rationality, and unfit data were excluded.

Calculation and validation of OHIZ

The OHIZ algorithm from the robust global One Health index (GOHI) algorithm system that was reported recently [22, 23], was used to estimate OHIZ. Indicator weights were determined by adopting the fuzzy analytical hierarchy process (FAHP) [34], followed by fuzzy comparison matrix formation [22, 23] (Additional file 1). For indicators with values of “0” or “1”, appropriate measures were taken to correct bias from overpolarization.

According to the WB classification criteria for countries and regions, there are 47 countries/territories in sub-Saharan Africa. OHIZ was analyzed for all sub-Sahara African countries (n = 47). Criteria of data of the same indicators from three similar countries were used to exclude biased data or countries with missing data. When there were more than 160 missing data points for an indicator, the indicator was excluded. When there were more than 50% missing data for a country, the country was excluded from the final list. A total of 37 sub-Sahara African countries were retained for the OHIZ (Fig. 2).

Fig. 2
figure 2

OHIZ overall scores of sub-Sahara African countries. A Sub-Sahara African countries OHIZ map. B OHIZ scores of sub-Sahara African countries. Data statistics included 37 sub-Sahara African countries. C Scores trend chart of sub-Sahara African countries for all-level indicators of OHIZ. Subindicators are denoted by standing initial as follows: SR strategy and regulation, SVR surveillance and response, SNT sanitation, DTT detection, VRI vector and reservoir interventions, VNR vaccination regulation, PCI population coverage and cost of interventions, IMS inhabitants below 5 m above sea level, HPZ health promotion for zoonoses, NPA natural protected areas, CCV cases of COVID-19, HDE human DALYs of echinococcosis; HDL human DALYs of leishmaniasis, HDR human DALYs of rabies, HDT human DALYs of tuberculosis. OHIZ One Health index on zoonoses


OHIZ indicators and datasets

This study identified 28 datasets for zoonoses under 15 subindicators and five OHIZ indicators, which were all associated with weights (Table 1). Among the five indicators, route of transmission scored the highest (25.31%) weight, followed by source of infection (23.70%), targeted population (19.09%), and capacity building (16.77%). Outcomes (case studies) accounted only 15.13%. Weight for subindicators was also estimated according to the weight calculation. For example, in the source of infection, the strategy and regulation subindicator weighted 41.32%; in the route of transmission, the vector and reservoir interventions subindicator was estimated at 54.85%; in the targeted population, the population coverage and cost of interventions subindicator weighted 39.43%; in capacity building, the subindicator of health promotion for zoonoses was 56.86%; and in outcomes (case studies), the COVID-19 subindicator weighted 25.42%, out of three, two, three, two, and five subindicators under each of the indicators, respectively. Meanwhile, the weights for the datasets were attributed on average (Table 1).

Sub-Saharan Africa scores for OHIZ

A total of 37 sub-Sahara African countries qualified for OHIZ score evaluation, after 10 countries that did not meet the inclusion criteria were excluded. Figure 2A shows the 37 countries/territories with different shades of red color, which reflected ranges of scores. The Country scores for subindicators are shown in Fig. 2B, C. The average score of sub-Sahara African countries was 53.67, with better scores in subindicators of surveillance and response, vector and reservoir interventions, natural protected areas, and leishmaniasis control. South Africa had the highest score (71.99), suggesting that the country has a strong capacity in responding to or preventing zoonotic events. South Africa, Mauritius, Rwanda, Botswana, Mali, Tanzania, Nigeria, Kenya, Cote d’Ivoire, and Ethiopia were the top 10 countries that had better OH performance for zoonoses, while Benin had the lowest score (40.51) (Table 2).

Table 2 Indicator scores of OH performance on zoonoses in sub-Saharan Africa. Ranks included 37 sub-Sahara African countries

Indicator scores of OHIZ in the sub-Sahara African countries

Sub-Sahara African countries performed best in the indicator of capacity building for zoonoses (76.80 ± 8.25), overall. The indicator scores obtained for the route of transmission, source of infection, and outcomes (case studies) were 60.64 ± 12.43, 55.41 ± 12.52, and 35.27 ± 10.03, respectively (Fig. 3A). The OHIZ score for the indicator of the targeted population (33.33 ± 28.87) was the lowest, overall, while it was not normally distributed across the region. Following the indicator scores, South Africa had the highest score (89.03), while Benin received the lowest performance capacity (27.66) in responding to or preventing the source of infection (Fig. 3B). For performance capacity in responding to or preventing the route of transmission, the highest score was by Togo (81.15), and the lowest score was achieved by to Cabo Verde (33.79) (Fig. 3C). Mali scored highest (70.39) in capacity for the targeted population indicator, while Benin scored lowest (15.29) (Fig. 3D). For performance in capacity building for zoonoses, Sierra Leone ranked first (90.23), while the lowest score was by Chad (57.75) (Fig. 3E). In terms of outcomes (case studies) for zoonoses, Mauritius had the highest performance (69.13) in responding to or preventing tuberculosis, COVID-19, echinococcosis, leishmaniasis, and rabies collectively, whereas the Chad ranked lowest (18.86) (Fig. 3F). Details are provided in Table 2.

Fig. 3
figure 3

OHIZ scores density of sub-Sahara African countries. A Score density of OHIZ indicators in sub-Saharan Africa. BF Scores of OHIZ indicators across sub-Saharan Africa. Sub-Sahara African countries were ranked from left to right according to indicator scores. ZAF South Africa, MUS Mauritius, RWA Rwanda, BWA Botswana, MLI Mali, TZA Tanzania, NGA Nigeria, KEN Kenya, CIV Cote d’Ivoire, ETH Ethiopia, TGO Togo, BFA Burkina Faso, SYC Seychelles, CMR Cameroon, MOZ Mozambique, MDG Madagascar, GIN Guinea, COD0 Democratic Republic of Congo, SEN Senegal, NAM Namibia, ZWE Zimbabwe, UGA Uganda, GHA Ghana, SDN Sudan, MWI Malawi, SLE Sierra Leone, NER Niger, LSO Lesotho, GAB Gabon, ZAM Zambia; BDI Burundi, CPV Cabo Verde, LBR Liberia, TCD Chad, CAF Central African Republic, MRT Mauritania, BEN Benin. OHIZ One Health index on zoonoses

Subindicator scores of the OHIZ in the sub-Sahara African countries

The average capacity in strategy and regulation to respond to and prevent sources of infection for sub-Sahara African countries was 39.55 for national guidelines for surveillance/control, while it was 17.24 and 80.64 for national legislation on animal reservoirs and zoonoses capacity scores, respectively. In addition, the average scores of sub-Sahara African countries in surveillance and response to source of infection were 85.83 for general surveillance, 80.84 for vector control, and 69.59 for wildlife reservoirs control, while in sanitation, the average capacity was estimated at 40.32 for basic sanitation services (Fig. 4B).

Fig. 4
figure 4

Dataset scores of OHIZ in sub-Sahara African countries. A Score scatter of OHIZ indicators. Data statistics included 37 sub-Sahara African countries. Indicators are denoted by standing initial as follows: SI, source of infection; RT, route of transmission; TP, target population; CB, capacity building; CS, outcomes (case studies). BF Score scatter of OHIZ datasets. Data statistics included 37 sub-Sahara African countries. Datasets are denoted by standing initial as follows: NGS National guideline for surveillance/control, NLR National legislation on animal reservoirs, ZCS Zoonoses capacity score, GSV General surveillance; VTC Vector control, WRC Wildlife reservoirs control, BSS Basic sanitation services, LTR Laboratory testing for zoonotic reservoirs (vectors and animals), PAN Policy adoption of insecticide-treated mosquito nets; PAS Policy adoption of indoor residual spraying, PCC Prevention chemotherapy coverage of zoonoses, VSR Vaccination strategy and regulation vaccination, PPF Proportion of population having basic drinking water, CCV Costs directed to chemotherapy/vaccination of humans, NIL Number of inhabitants below 5 m above sea level, LEA Legislation of zoonosis educational activities, PCZ Prevention and control of zoonoses; NPV National plan for zoonoses vaccine, ZEI Zoonotic events and human-animal interface, EWZ Early warning for zoonoses; ESS Emergency/surveillance system, NPA Proportion of natural protected areas, INC Infections number of COVID-19, VCC Vaccination coverage for COVID-19, ECD Echinococcosis DALYs, LMD Leishmaniasis DALYs, RBD Rabies DALYs, TBD Tuberculosis DALYs. OHIZ One Health index on zoonoses

The average score for detection in responding to and preventing the route of transmission for laboratory testing for zoonotic reservoirs (vectors and animals) in sub-Sahara African countries was 45.05. The average score for policy adoption of insecticide-treated mosquito nets was 90.93, that for policy adoption of indoor residual spraying was 85.68 and that for prevention chemotherapy coverage of zoonoses was 43.81 in terms of capacity of vector and reservoir interventions (Fig. 4C).

Sub-Saharan Africa scored 67.46 for costs directed to chemotherapy/vaccination of humans and 41.79 for proportion of population having basic drinking water and sanitation facilities in terms of capacity performance for population coverage and cost of interventions, while the average score was 37.52 for vaccination strategy and regulation vaccination in capacity of vaccine for target population (Fig. 4D).

The average scores of sub-Sahara African countries in capacity building were 57.42 for legislation of zoonosis educational activities, 75.49 for zoonoses prevention and control, 77.35 for zoonoses vaccine national plan, 54.59 for zoonotic events and the human-animal interface, 48.39 for zoonotic early warning, and 65.77 for emergency/surveillance system in health promotion for zoonoses, while it was 94.77 for proportion of natural protected areas in terms of natural protected areas building (Fig. 4E).

The OH index for the five zoonotic diseases that were assessed in this study, revealed the highest score of human DALYs of leishmaniasis (66.15), followed by those of echinococcosis (42.20), rabies (19.19), and tuberculosis (17.31). However, the capacity of sub-Sahara African countries in responding to and preventing COVID-19 scored an average vaccination coverage estimated at 47.69 and that of infectious number at 34.56 (Fig. 4F).


This study used a newly established evaluation system, GOHI, to assess OH performance for zoonoses through scores of indicators, and provided essential guidance and references for zoonotic event prevention and control in sub-Saharan Africa.

The OHIZ datasets built in this study referred to relatively complete data for zoonoses, from international organizations and authoritative databases, such as WHO, OIE-WHAIS, FAO-EMPRES, WB, GHS, and GBD. Following the very recently developed assessment tool for OH performance [22, 23], we used indicators based on guidelines for OH and zoonoses, and generated OHIZ datasets that fit to research approaches for zoonoses from a global and holistic view.

The algorithm used for OHIZ in this study referred to the GOHI algorithm system [34], which provided scores for 37 sub-Sahara African countries out of the 48. A total of 11 countries/territories excluded from this study were of low quality or presented insufficient data for OHIZ calculation [23]. However, such an exclusion suggests that publicly available data that would reflect application of the OH approach for zoonoses control and prevention are needed.

Throughout the OHIZ scores identified by this study, only South Africa exceeded 70 in scores (71.99). In addition, five countries exceeded 60 in scores, and 12 countries were lower than 50 in scores, while the scores of all the countries were normally distributed on the whole (Fig. 2B). South Africa performed best in the source of the infection indicator and ranked first in the height subindicators, along with scores above average in 22/28 subindicators. This suggests that OH initiatives for zoonoses, including capacity in surveillance and research activities, are being successfully implemented in this country [21, 35]. Mauritius ranked second behind South Africa for overall scores for zoonoses, with good performances in 3/5 indicators including the selected zoonotic case studies (rank 1), source of infection (rank 2), and targeted population (rank 3). Such performance aligns with the results following implementations of the strategic partnership for health security and emergence preparedness by the country in collaboration with the WHO and other international organizations [35]. Most had inconsistent performances in different indicators and subindicators, suggesting the implementation of some components of the OH approach to zoonoses. For example, Mali, Togo, and Sierra Leone ranked first, fifth, and twenty-sixth for overall scores, respectively. Remarkably, they received the highest score (first rank) for indicators of source of infection (Mali), route of transmission (Togo), and targeted population (Sierra Leone). These findings are consistent with (i) the OH project recently established in collaboration with the Swiss Tropical and Public Health Institute to tackle sources of zoonotic infections, e.g., as rabies, in Mali [20]; (ii) improvement in cross-border preparedness and response to zoonotic diseases in Togo [36]; and (iii) the establishment of national multisectoral coordination and collaboration mechanisms to prevent, detect, and respond to public health threats in Sierra Leone [37], especially after the bitter experience of the Ebola outbreak response, which served as an important catalyst for increased efforts to build the country’s capacity for health security and emergence preparedness. Furthermore, Benin ranked first in three subindicators and a number of height subindicators obtained above average scores, reflecting the country’s better performance in indicators of route of transmission and in zoonotic case studies than that of South Africa. This suggests that, despite its lowest overall score for zoonoses, Benin performed better for zoonotic disease control, especially in responding better to tuberculosis, COVID-19, echinococcosis, leishmaniasis, and rabies than South Africa did.

Results from this study provided OH performance for zoonoses and promoted awareness of OH by providing a reference in OH practice, research gaps and international assistance for sub-Sahara African countries. In addition, this study revealed an important finding where the governments of the 11 countries that were excluded are encouraged to direct more resources in the holistic application of the One Health approach for zoonoses so that their performance can be assessed in future. However, this work based on the OHIZ framework and official databases from authoritative organizations, which might be restrictive for the selection of the indicators within the scope of the framework.

In the last two decades, human beings have suffered from zoonoses. Zoonoses prevention and control had issues such as cross-border transmission and multidisciplinary integration. The OH approach provides an opportunity to overcome these challenges. In addition, the development of OH between countries needs to be synchronized. The OH concept has been raised at the beginning of the twenty-first century [38, 39] and has gained much more attention in recent years [40]. However, OH practices are still ignored at both the government and local levels, a cohesive network able to receive and act on early warnings at different levels is missing. The potential challenges that the OH approach will encounter when being implemented worldwide would be the establishment of efficient interdepartmental collaboration mechanisms and multidisciplinary platforms to support governance capacity in surveillance and research activities. Therefore, national and regional multisectoral coordination and collaboration mechanisms between medical doctors, veterinarians, public health experts, and food quality inspectors are needed to improve detection and responses to the public health matters holistically.


Indicators to assess OH performance related to zoonoses are manifold, yet they are still not seemingly being embraced in developing countries, especially in sub-Saharan Africa, where zoonoses have the greatest impact. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen OH strategies for effective control of zoonoses and to support prevention of a next zoonotic event.

Availability of data and materials

The full study protocol and the datasets, are available, following manuscript publication, upon request from the corresponding author Kokouvi Kassegne (



Acquired immune deficiency syndrome






Burkina Faso




Central African Republic


Cases of COVID-19


Cote d’Ivoire




Democratic Republic of Congo


Novel coronavirus disease


Cabo Verde


Disability-adjusted life years






Fuzzy analytical hierarchy process


United Nations’ Food and Agriculture Organization




Global burden of disease




Global health data exchange


Global health security




Global One Health index


Human DALYs of echinococcosis


Human DALYs of leishmaniasis


Human DALYs of rabies


Human DALYs of tuberculosis


Health promotion for zoonoses


Inhabitants below 5 m above sea level










Middle East respiratory syndrome


















Natural protected areas


One Health


One Health high-level expert panel


One Health index on zoonoses


World Organization for Animal Health


Population coverage and cost of interventions




Severe acute respiratory syndrome






Sierra Leone




Surveillance and response












United Nations Environment Programme


Vaccination regulation


Vector and reservoir interventions


World Bank


World Health Organization


South Africa






  1. Samiran P, Balram B, Mohan DG. One world one health: widening horizons. Indian J Med Res. 2021;153(3):241–3.

    Article  Google Scholar 

  2. Giulia R, Luigi B, Eleonora C, Chiara S, Maryam S, Ramin T, et al. Emerging zoonoses: the “one health approach.” Saf Health Work. 2012;3(1):77–83.

    Article  Google Scholar 

  3. WHO: FAO, OIE, and WHO launch a guide for countries on taking a One Health approach to addressing zoonotic diseases. 2019. Accessed 08 Apr 2022.

  4. FAO, OIE, and WHO launch new online training on navigating the Tripartite Zoonoses Guide (TZG). 2020. Accessed 15 Apr 2022.

  5. WHO: New international expert panel to address the emergence and spread of zoonotic diseases. 2021. Accessed 08 Apr 2022.

  6. WHO: 26 International experts to kickstart the One Health High Level Expert Panel (OHHLEP). 2021. Accessed 08 Apr 2022.

  7. WHO: Tripartite and UNEP support OHHLEP's definition of "One Health". 2021. Accessed 08 Apr 2022.

  8. Guha D, Ruiyun L, Emmanuel C, Katharine RD, Rodolfo D, Kjetill SJ, et al. The animal origin of major human infectious diseases: what can past epidemics teach us about preventing the next pandemic? Zoonoses. 2022;2:11.

    Google Scholar 

  9. Emerging zoonoses: A one health challenge. EClinicalMedicine. 2020; 19:100300.

  10. Md TR, Md AS, Md SI, Samina I, Md JH, Mohamed EZ, et al. Zoonotic diseases: etiology, impact, and control. Microorganisms. 2020;8(9):1405.

    Article  Google Scholar 

  11. Marli CC, Michely BR, Nicholas AM, Lorendane MC, Rodrigo SB. Emerging and re-emerging human infectious diseases: a systematic review of the role of wild animals with a focus on public health impact. Asian Pac J Trop Med. 2020;13(3):99–106.

    Article  Google Scholar 

  12. ILRI. Mapping of poverty and likely zoonoses hotspots. Zoonoses Project 4. Report to Department for International Development, UK. ILRI, Nairobi, Kenya (2012) 119 pp. Accessed 06 May 2022.

  13. Jakob Z, Jürg U, Nicole PH, Shan L, Zhou XN. Towards integrated surveillance-response systems for the prevention of future pandemics. Infect Dis Poverty. 2020;9(1):140.

    Article  Google Scholar 

  14. Walter LF, Linda T, Sanika AP, Marina K, Gustavo JN. Climate change and zoonoses: a review of concepts, definitions, and bibliometrics. Int J Environ Res Public Health. 2022;19(2):893.

    Article  Google Scholar 

  15. Aashaq HB. Bacterial zoonoses transmitted by household pets and as reservoirs of antimicrobial resistant bacteria. Microb Pathog. 2021;155: 104891.

    Article  Google Scholar 

  16. Folorunso OF, Olubunmi GF, Yilma JM, Charles B, Bernard B, Kristina R. The one health landscape in Sub-Saharan African countries. One Health. 2021;13:100325.

    Article  Google Scholar 

  17. Peninah MM, Eric MO, Clayton OO, Austine OB, Athman M, et al. Successes and challenges of the One Health approach in Kenya over the last decade. BMC Public Health. 2019;19(Suppl 3):465.

    Google Scholar 

  18. Esther B, Edwinah A, Peninah N, David M, Irene N, John DK, et al. Operationalizing the One Health approach in Uganda: challenges and opportunities. J Epidemiol Glob Health. 2020;10(4):250–7.

    Article  Google Scholar 

  19. Lisa C, Maya W, Ophelie L, Mark N, Marie M, Mirgissa K, et al. One hundred years of zoonoses research in the Horn of Africa: a scoping review. PLoS Negl Trop Dis. 2021;15(7):e0009607.

    Article  Google Scholar 

  20. Monique L, Abdallah T, Jan H, Vessaly K, Assandi O, Mathilde T, et al. Increasing rabies data availability: the example of a One Health research project in Chad Côte d’Ivoire and Mali. Acta Trop. 2021;215: 105808.

    Article  Google Scholar 

  21. Greg S, Fabiola Q, Pranab C, Manish K, Matthew FC, Séverine T. Research priorities for control of zoonoses in South Africa. Trans R Soc Trop Med Hyg. 2021;115(5):538–50.

    Article  Google Scholar 

  22. Zhang XX, Liu JS, Han LF, Geoff S, Guo XK, Zhou XN. One Health: new evaluation framework launched. Nature. 2022;604(7907):625.

    Article  CAS  Google Scholar 

  23. Zhang XX, Liu JS, Han LF, Xia S, Li SZ, Li OY, et al. Towards a global One Health index: a potential assessment tool for One Health performance. Infect Dis Poverty. 2022;11(1):57.

    Article  Google Scholar 

  24. Norbert GS, Ulrike L, Andreas H, Rebecca H, Andreas EZ, Daniel E, et al. Microbiological laboratory diagnostics of neglected zoonotic diseases (NZDs). Acta Trop. 2017;165:40–65.

    Article  Google Scholar 

  25. Mahmoud I, Adel RM, Mina Y, Sudharsana RA, Javad A, Emilia W, et al. Apoptosis, autophagy and unfolded protein response pathways in Arbovirus replication and pathogenesis. Expert Rev Mol Med. 2016;18: e1.

    Article  Google Scholar 

  26. Toph A, Kris AM, Carlos ZT, Stephen SM, Carlo R, Moreno DM, et al. Global hotspots and correlates of emerging zoonotic diseases. Nat Commun. 2017;8(1):1124.

    Article  Google Scholar 

  27. Michel MVI. FDG-PET as a biomarker in lymphoma: from qualitative to quantitative analysis. Hematol Oncol. 2015;33(Suppl 1):38–41.

    Google Scholar 

  28. Evelina T, Elena C, Alessia S, Stephan H, Marc M, Dominique LM, et al. WHO Pathogens Priority List Working Group, Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis. 2018;18(3):318–27.

    Article  Google Scholar 

  29. David F, Christopher S, Anne HB, Kristina S. Internet-delivered cognitive behavioral therapy for problematic alcohol use in a workplace setting: protocol for quantitative and qualitative evaluation of feasibility and outcomes. JMIR Res Protoc. 2020;9(7): e18693.

    Article  Google Scholar 

  30. Adam W, Patrick E, Sven N, Marc FS. Introducing the World Bank’s 2018 health equity and financial protection indicators database. Lancet Glob Health. 2019;7(1):e22–3.

    Article  Google Scholar 

  31. Sophie MR, Michael P, Christopher I, Jessica B, Amanda S, Arnold H, et al. Analysing COVID-19 outcomes in the context of the 2019 Global Health Security (GHS) Index. BMJ Glob Health. 2021;6(12): e007581.

    Article  Google Scholar 

  32. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789–858.

    Article  Google Scholar 

  33. Zhou MG, Wang HD, Zeng XY. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58.

    Article  Google Scholar 

  34. Ertugrul A. Interval valued intuitionistic fuzzy analytic hierarchy process-based green supply chain resilience evaluation methodology in post COVID-19 era. Environ Sci Pollut Res Int. 2021.

    Article  Google Scholar 

  35. WHO: Strategic Partnership for Health Security and Emergence Preparedness Portal. South Africa.” Accessed 6 May 2022.

  36. Kakaī CG, Okunromade OF, Dan-Nwafor CC, Chabi AIB, Martial GTP, Dalhat MM, et al. Improving cross-border preparedness and response: lessons learned from 3 Lassa fever outbreaks across Benin, Nigeria, and Togo, 2017–2019. Health Secur. 2020;18(S1):S105–12.

    Article  Google Scholar 

  37. Agbo S, Gbaguidi L, Biliyar C, Sylla S, Fahnbulleh M, Dogba J, et al. Establishing National Multisectoral Coordination and collaboration mechanisms to prevent, detect, and respond to public health threats in Guinea, Liberia, and Sierra Leone 2016–2018. One Health Outlook. 2019;1:4.

    Article  Google Scholar 

  38. John SM, Martyn J. The One Health approach-why is it so important? Trop Med Infect Dis. 2019;4(2):88.

    Article  Google Scholar 

  39. Ilaria C, Giovanni C. One Health (r)evolution: learning from the past to build a new future. Viruses. 2018;10(12):725.

    Article  Google Scholar 

  40. Chen GQ. To develop theory and practice of One Health is imperative in China. Sci Technol Rev. 2020;38(5):1 (in Chinese).

    Google Scholar 

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The authors especially thank experts from the World Health Organization, the Food and Agriculture Organization of the United Nations, The World Bank, and universities or institutions for their critical comments and suggestions during the analysis and interpretation of the data. The authors particularly extend their acknowledgments to Dr. Jun-Xia Song from the Food and Agriculture Organization of the United Nations and to Prof. Xiao-Nong Zhou from the School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University-The University of Edinburgh, Shanghai, who critically commented the manuscript for intellectual content.


The study was financially supported by the China Medical Board (Grant No. 20-365), the Intradisciplinary Platform Fund of Shanghai Jiao Tong University (Grant No. 20200927), and the Young Faculty Start-up Project Fund of Shanghai Jiao Tong University (Grant No. 21X010501074). The sponsor played no roles in the study design; in the collection, analysis, or interpretation of the data; in writing the report; or in the decision to submit the article for publication.

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Authors and Affiliations



Conception and design: KK. Data collection: H-QZ, S-WF, J-XY, QL, T-GJ, YZ, and KK. Data analysis: H-QZ, J-BX, Z-YG, L-FH, X-XZ, and SX. Supervision: YZ and KK. Writing-original draft: H-QZ, S-WF, and J-XY. Writing-review & editing: J-XS, X-KG and KK. All authors read and approved the final manuscript.

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Correspondence to Kokouvi Kassegne.

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The authors have no other competing interests to disclose.

Supplementary Information

Additional file 1.

Sources of data for the indicator sets used for zoonoses OHi.

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Zhao, HQ., Fei, SW., Yin, JX. et al. Assessment of performance for a key indicator of One Health: evidence based on One Health index for zoonoses in Sub-Saharan Africa. Infect Dis Poverty 11, 109 (2022).

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  • One Health index
  • One Health performance
  • Zoonoses
  • Sub-Saharan Africa