Highlights
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Seroprevalences were found to be 12.4% for dengue virus, 10.7% for Chikungunya virus, and 10.2% for West Nile virus.
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Namanga cross border had highest immunoglobulin M of dengue virus: 9.7%, Chikungunya virus: 6.9%, and West Nile virus: 6.3%.
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International travel and mosquito exposure were associated with arboviral positivity.
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Gender and education level influenced odds of arboviral seropositivity.
Keywords: Chikungunya, Dengue, Yellow fever, Seroprevalence, Risk factors, Tanzania
Abstract
Objectives
Dengue (DENV), Chikungunya (CHIKV), and West Nile (WNV) viruses are among the major mosquito-borne arboviral threats in East Africa. This study assessed seroprevalence and risk factors for DENV, CHIKV, and WNV among residents of Mutukula and Namanga cross borders in Tanzania.
Methods
Blood samples were collected from 450 participants and tested using an enzyme-linked immunosorbent assay for anti-viral immunoglobulin G and M (IgG and IgM) antibodies. Descriptive and multivariate logistic regression analyzed risk factors.
Results
Overall seroprevalence was 12.4% for DENV, 10.7% for CHIKV, and 10.2% for WNV. Namanga had higher IgM seroprevalences (DENV: 9.7%, CHIKV: 6.9%, and WNV: 6.3%) than Mutukula (DENV: 5.6%, CHIKV: 5.3%, WNV: 5. 9%). IgG seropositivity for CHIKV was linked to a history of travel abroad (adjusted odds ratio [AOR] 7.3, 95% confidence interval [CI] 1.01-54.6), mosquito exposure (AOR 2.8, 95% CI 1.01-7.7), and being female (AOR 6.1, 95% CI 1.3-28). IgM seropositivity was influenced by education, with secondary levels reducing risk (AOR 0.11, 95% CI 0.02-0.64 for DENV; AOR 0.06, 95% CI 0.1-0.6 for CHIKV).
Conclusion
This study provides the first evidence of WNV exposure in Tanzania and specific cross-border seroprevalences for DENV and CHIKV transmission. To mitigate the risks, Integrated vector control and enhanced surveillance are highly recommended.
Introduction
Mosquito-borne arboviruses, including yellow fever virus (YFV), dengue virus (DENV), Chikungunya virus (CHIKV), and West Nile virus (WNV), pose significant public health challenges in tropical and subtropical regions [1]. In East Africa, favorable climatic conditions and high mosquito vector density drive rapid transmission, with severe outcomes ranging from dengue hemorrhagic fever to WNV encephalitis and YFV-induced hepatitis [2,3]. These viruses threaten global health security, particularly in regions with limited surveillance and vector control measures [4].
YFV, DENV, and WNV belong to the Flaviviridae family, while CHIKV is a Togaviridae member [5]. DENV infections range from mild fever to severe dengue hemorrhagic fever, especially upon reinfection [6]. CHIKV causes debilitating arthralgia, headaches, and rashes, often persisting for months [7]. YFV leads to severe hepatitis and hemorrhagic fever, while WNV, typically mild, can cause encephalitis in rare cases [8].
Effective vector control is critical to interrupt transmission by infected mosquitoes, primarily Aedes and Culex species [9].
Tanzania has experienced recurrent DENV outbreaks since 2010, and CHIKV has been reported in northern, southern, and central regions [10,11]. Recent seroprevalence studies have reported low-level YFV exposure in Tanzania, while WNV remains undocumented locally [12]. Recurrent YFV outbreaks in Uganda (2016) and WNV detections in Kenya underscore the region’s susceptibility [13,14]. The absence of confirmed YFV clinical cases and WNV in Tanzania suggests potential underdiagnosis, necessitating enhanced surveillance, especially at cross-border zones [15].
The Namanga (Tanzania–Kenya) and Mutukula (Tanzania–Uganda) border areas are epidemiological hotspots due to high population mobility facilitated by the East African Community (EAC) protocol, which permits movement of cross-border residents with minimum immigration and health restrictions beyond 10 km for travel, trade, and social interactions [16]. This cross-border movement, coupled with ecological factors such as water bodies and mosquito breeding sites, creates a conducive environment for arbovirus transmission [17]. Despite known DENV and CHIKV circulation in the country, limited seroprevalence data on their circulation, including undocumented occurrence of WNV in these cross-border zones, raise concerns about undetected transmission [14,18].
This study aimed to determine the seroprevalence and associated risk factors of DENV, CHIKV, and WNV among residents within 10 km of the Namanga and Mutukula cross borders.
Materials and methods
Study area and study design
This study was conducted at two major international border regions: the Mutukula border in Kagera, which borders Tanzania and Uganda, and the Namanga border in the Arusha region, bordering Tanzania and Kenya (Figure 1). These borders were strategically selected due to their ecological and socio-economic diversity, as well as their geographic and climatic characteristics, such as variations in altitude, rainfall patterns, and local economies. These factors are crucial for understanding the risks associated with arboviruses. A detailed description of these areas has been published elsewhere [19].
Figure 1.
Map showing the study location.
Specific hamlets included Kabwoba, Mgambaizi, Chadimba, Kasharu, Kateebe, and Byamtemba in Mutukula, along with Buguruni, Mtaa wa Saba, Kisongo, Lesoti, Skarda, and Eworendeke in Namanga. These locations were included because they were within the 10 km buffer zones. While the previous study [19] focused solely on Yellow Fever, this research expanded to include DENV, CHIKV, and WNV.
Sample size estimation
Using a fever prevalence of 50%, a desired precision of 5%, and a 95% confidence level, the minimum estimated sample size was 332. To account for non-responses, refusals, or absences, a rate of 17.2% was added. The final sample size was adjusted to 450 participants. This sample was proportionally distributed between the two border areas, with one-third coming from Namanga and two-thirds coming from Mutukula. Within each border region, there was equal representation of participants from healthcare facilities and households.
Data and sample collection
The study was conducted between June and July 2023. The research team was trained on study objectives, ethical guidelines, and data collection tools, and was directly involved in participant sampling and data collection at the study sites. The study’s purpose, significance, and sampling approach were explained to officials at regional, district, ward, and village levels, as well as to participants, before obtaining informed consent. Participants included individuals aged 9 months and older. Infants under 9 months were excluded to avoid interference from maternally acquired antibodies. Foreign nationals were also excluded to ensure the findings reflected local arbovirus exposure.
Data were collected using a pre-tested semi-structured questionnaire through face-to-face interviews conducted in Kiswahili (the national language of Tanzania). Sociodemographic data collected included age, sex, occupation, village, workplace, and area of residence. Information on potential exposure risks focused on factors such as international travel history, the presence of mosquitoes in the surroundings, and visits to forested areas. Blood samples were obtained from consenting outpatients at healthcare facilities and community members at households. Adults and children aged 10 years and above provided 5 ml of blood, while younger children provided 2 ml.
Laboratory examination
Serum was separated from collected blood samples by centrifugation at district hospitals. Each sample was labeled with a unique identification number, archived, and stored in liquid nitrogen (−196°C) before transfer to the National Public Health Laboratory. At the laboratory, samples were stored at −80°C until analysis. Aliquots of serum samples were tested for immunoglobulin (Ig)G and IgM antibodies against DENV, CHIKV, and WNV using commercial enzyme-linked immunosorbent assay (ELISA) kits (Sunlong Biotech, Inc., Hong Kong). The testing protocol adhered to the manufacturer’s instructions, as described in detail elsewhere [19]
Data analysis
Data were downloaded from the Kobo Toolbox servers as Microsoft Excel files, cleaned, and exported to Epi Info version 7.2.5.0 for analysis. Numerical variables were summarized using medians and interquartile ranges, while categorical variables were analyzed using cross-tabulations and chi-square tests. Logistic regression was performed to identify the odds of arbovirus exposure, with statistical significance affirmed when P <0.05. Statistical cut-off values for ELISA antibody detection were calculated based on the manufacturer’s instructions, using the average optical density value of the negative control wells plus 0.15. The assay was considered reliable if the intra-assay and inter-assay coefficients of variability (CV) were less than 15%.
Ethical considerations
The study protocol was approved by the Tanzania Medical Research Coordinating Committee of the National Institute for Medical Research (Ref no: NIMR/HQ/R.8a/Vol IX/4325). Participants were informed about the study’s objectives, potential benefits, and risks before providing written consent or assent. Ethical principles, including confidentiality, voluntary participation, and respect for participants' rights, were strictly upheld throughout the study.
Results
Demographic characteristics of the study population
Demographics of the study population are summarized in Table 1. A total of 450 subjects were enrolled, with 144 (32%) participants from Namanga and 306 (68%) from Mutukula. A total of 278 (61.5%) were female and 172 (38.5%) were male. The median age was 24 years with an interquartile range from 23 to 38 years.
Table 1.
Sociodemographic characteristics of study participants by Border (n = 450).
| Border | Namanga | Mutukula | Total | |
|---|---|---|---|---|
| Sex | Male | 42 (9.3%) | 130 (28.9%) | 172 (38.2%) |
| Female | 102 (22.7%) | 176 (39.1%) | 278 (61.8%) | |
| Age (in years) | <28 | 64 (14.2%) | 157 (34.9%) | 221 (49.1%) |
| 28-42 | 45 (10%) | 91 (20.2%) | 136 (30.2%) | |
| >42 | 35 (7.8%) | 58 (12.9%) | 93 (20.7%) | |
| Education | Primary | 64 (14.2%) | 224 (49.8%) | 288 (64.0%) |
| Secondary | 36 (8.0%) | 42 (9.3%) | 78 (17.3%) | |
| Post-secondary | 17 (3.8%) | 8 (1.8%) | 25 (5.6%) | |
| None | 27 (6.0%) | 32 (7.1%) | 59 (13.1%) | |
| Main occupation | Crop farming | 43 (9.6%) | 94 (20.9%) | 137 (30.4%) |
| Business | 14 (3.1%) | 28 (6.2%) | 42 (9.3%) | |
| Formal employment | 19 (4.2%) | 45 (10.0%) | 64 (14.2%) | |
| Non-formal employment | 30 (6.7%) | 62 (13.8%) | 92 (20.5%) | |
| Livestock keeping | 16 (3.6%) | 32 (7.1%) | 48 (10.7%) | |
| Student | 22 (4.9%) | 45 (10.0%) | 67 (14.9%) | |
| Body Temperature | ≤37.4°C | 112 (24.9%) | 244 (54.2%) | 356 (79.1%) |
| 37.5°C-38.0°C | 18 (4.0%) | 46 (10.2%) | 64 (14.2%) | |
| >38°C | 9 (2.0%) | 6 (1.3%) | 15 (3.3%) | |
| History of travel to neighboring countries | Yes | 124 (35.4%) | 211 (46.9%) | 335 (74.4%) |
| No | 20 (4.4%) | 95 (21.1%) | 115 (25.6%) |
Seroprevalence of DENV, CHIKV, and WNV
A total of 450 serum samples were tested for IgG and IgM antibodies specific to DENV, CHIKV, and WNV. The overall seroprevalence rates for DENV, CHIKV, and WNV were 12.4%, 10.7%, and 10.2% respectively. Variations in seroprevalence rates were observed between the two border locations, Mutukula and Namanga, although the differences were not statistically significant. For DENV, although the Namanga border had a higher IgM seroprevalence rate of 9.5%, Mutukula also reported a higher IgG seroprevalence at 5.9%. The seroprevalence for CHIKV was also higher in Namanga for both IgM and IgG (6.9% and 6.3%, respectively), compared to Mutukula’s 5.3% IgM and 4.3% IgG. Similarly, for WNV, Namanga had higher rates of 6.3% IgM and 4.9% IgG, compared to 5.9% IgM and 3.9% IgG observed at Mutukula (Table 2).
Table 2.
Prevalence of seropositivity of IgM and IgG by border.
| Pathogen | Namanga (n = 144) (IgM) |
Mutukula (n = 306) (IgM) |
P-value | Namanga (n = 144) IgG |
Mutukula (n = 306) IgG |
P-value |
|---|---|---|---|---|---|---|
| DENV | 14 (9.7) | 17(5.6) | 0.156 | 7(4.9) | 18(5.9) | 0.8194 |
| CHIKV | 10 (6.9) | 16(5.3) | 0.615 | 9(6.3) | 13(4.3) | 0.4987 |
| WNV | 9 (6.3) | 18(5.9) | 1 | 7(4.9) | 12(3.9) | 0.8382 |
| DENV+CHIKV | 8 (1.8) | 5(1.2) | 0.044a | 11(2.5) | 16(3.5) | 0.4337 |
CHIKV, Chikungunya virus; DENV, dengue virus; Ig, immunoglobulin; WNV, West Nile virus.
P <0.05.
Evidence of co-occurrence of two-pathogen exposure was noted, with the highest co-infection rate of IgM observed between DENV and CHIKV at 13 (3.0%). For IgG, co-infections were reported between DENV and CHIKV in 27 (6.0%) participants. No co-infections involving WNV were detected (Table 2).
Demographic, clinical, and risk factors associated with anti-arboviral immunoglobulin G seropositivity
The logistic regression analysis for IgG antibodies highlights the demographic, clinical, and risk factors influencing seropositivity across the surveyed population (Table 3). Gender emerges as a significant factor; females exhibited higher odds of IgG seropositivity compared to males, with a significant adjusted odds ratio (AOR) of 6.1 for WNV IgG (95% confidence interval [CI]: 1.3-28, P <0.05) and 3.8 for DENV (95% CI: 1.18-11.9, P <0.05). Age also played a pivotal role, particularly for DENV, where individuals aged 28-42 were found to have a 3.6-fold increase in the odds of IgG seropositivity (AOR = 3.6, 95% CI: 1.4-9.1, P <0.001) compared to younger individuals.
Table 3.
Multivariate logistic regression model to predict anti-arboviral seropositivity (IgG) by social demographic, clinical and risk factors.
| Predictor variables | Levels | DENV (IgG) |
CHIKV (IgG) |
WNV (IgG) |
|||
|---|---|---|---|---|---|---|---|
| AOR (95% CI) | P-value | AOR (95% CI) | P-value | AOR (95% CI) | P-value | ||
| Age group | <28 years | Ref | Ref | Ref | Ref | Ref | Ref |
| 28-42 | 3.6 (1.4-9.1) | 0.01 | 1.7 (0.6-4.6) | 0.30 | 1.4 (0.48-4.4) | 0.52 | |
| >42 | 1.4 (0.4-4.8) | 0.63 | 1.8 (0.6-5.4) | 0.28 | 2.1 (0.6-6.4) | 0.19 | |
| Sex | Male | Ref | Ref | Ref | Ref | Ref | Ref |
| Female | 3.8 (1.18-11.9) | 0.02a | 1.9 (0.69-5.3) | 0.21 | 6.1 (1.3-28) | 0.02a | |
| Education | College | Ref | Ref | Ref | Ref | Ref | Ref |
| None | 0.53 (0.1-2.8) | 0.46 | 0.49 (0.11-1.99) | 0.32 | 0.61 (0.12-2.98) | 0.54 | |
| Primary | 0.62(0.15-2.5) | 0.49 | 0.25 (0.07-0.83) | 0.02a | 0.32 (0.1-1.3) | 0.11 | |
| Secondary | 0 (0-1.0) | 0.95 | 0 (0-1.0) | 0.97 | 0.11 (0.01-1.1) | 0.06 | |
| Occupation | Business | Ref | Ref | Ref | Ref | Ref | Ref |
| Farmer | 604,736 (0-1.0) | 0.97 | 131,310.9 (0-1.0) | 0.97 | 116,773 (0-1.0) | 0.97 | |
| Livestock | 578,493 (0-1.0) | 0.97 | 272,116.9 (0-1.0) | 0.97 | 313,779 (0-1.0) | 0.97 | |
| Other | 844,197 (0-1.0) | 0.97 | 274,293.8 (0-1.0) | 0.97 | 274,012 (0-1.0) | 0.97 | |
| Body temp (°C) | 37.4 | Ref | Ref | Ref | Ref | Ref | Ref |
| 37.5-38 | 0.56 (0.12-2.6) | 0.46 | 1.25 (0.40-3.8) | 0.69 | 0.64 (0.14-2.8) | 0.57 | |
| >38 | 0.84 (0.09-7.5) | 0.88 | 0 (0-1.0) | 0.97 | 0.0 (0-1.0) | 0.97 | |
| Place of living | Mutukula | Ref | Ref | Ref | Ref | Ref | Ref |
| Namanga | 0.82 (0.33-2.0) | 0.66 | 1.5 (0.63-3.6) | 0.36 | 1.3 (0.48-3.2) | 0.64 | |
| Clinical signs and symptoms | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Bleeding | 1.4 (0.09-19.1) | 0.81 | 0 (0-1.0) | 0.97 | 2.6 (0.22-28.8) | 0.44 | |
| Body malaise | 0.96 (0.17-5.3) | 0.96 | 0 (0-1.0) | 0.97 | 0.54 (0.06-4.8) | 0.58 | |
| Body rashes | 1.9 (0.29-12.9) | 0.50 | 1.27 (0.16-10.1) | 0.82 | 2.6 (0.45-14.6) | 0.29 | |
| Bruising | 2.8 (0.25-31.8) | 0.41 | 0 (0-1.0) | 0.97 | 0.0 (0-1.0) | 0.97 | |
| Headache | 2.7 (0.95-7.9) | 0.06 | 0.74 (0.21-2.6) | 0.64 | 0.88 (0.22-3.5) | 0.86 | |
| Muscle pain | 0.95 (0.21-4.3) | 0.95 | 1.29 (0.29-5.8) | 0.74 | 3.9 (0.95-16.0) | 0.06 | |
| Nause | 0.64 (0.06-7.2) | 0.71 | 0 (0-1.0) | 0.98 | 0.54 (0.05-5.5) | 0.61 | |
| Red eyes | 0 (0-1.0)0.72 | 0.97 | 0 (0-1.0) | 0.97 | 2.8 (0.19-41.0) | 0.45 | |
| Stomach ache | 0.91 (0.2-4.1) | 0.90 | 0.65 (0.08-5.03) | 0.68 | 0.43 (0.05-4.1) | 0.46 | |
| Jaundice | 2.1 (0.18-24.1) | 0.55 | 0 (0-1.0) | 0.97 | 2.8 (0.19-40.2 | 0.69 | |
| Vomiting | 1.5 (0.1-18.6) | 0.77 | 0 (0-1.0) | 0.97 | 1.6 (0.17-13.6) | 0.69 | |
| History of travel abroad | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 1.7 (0.59-5.26) | 0.31 | 7.3 (1.0-54.6) | 0.05a | 2.9 (0.65-12.6) | 0.16 | |
| Mosquitoes in the surroundings | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 1.2 (0.52-2.7) | 0.68 | 2.8 (1.01-7.7) | 0.05a | 1.1 (0.43-2.7) | 0.87 | |
| Visiting forest | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.57 (0.13-2.5) | 0.45 | 0.66 (0.15-2.9) | 0.38 | 1.3 (0.35-4.5) | 0.72 | |
AOR, adjusted odds ratio; CHIKV, Chikungunya virus; CI, confidence interval; DENV, dengue virus; Ig, immunoglobulin; WNV, West Nile virus.
P <0.05; Ref = reference group (AOR = 1).
Education level influenced CHIKV IgG, with those having primary education showing an AOR of 0.25 (95% CI: 0.07-0.83, P <0.05) compared to those with post-secondary education. History of travel abroad was also linked to higher odds of IgG seropositivity, especially for CHIKV (AOR 7.3, 95% CI: 1.01-54.6, P <0.05). Also, the presence of mosquitoes within the home environment increased the odds of IgG seropositivity, especially for CHIKV (AOR = 2.8, 95% CI: 1.01-7.7, P <0.01). The odds of IgG seropositivity also increased with some clinical characteristics, such as bruising for DENV (AOR = 2.8), body rashes for WNV (AOR = 2.6), headache for DENV (AOR = 2.7), red eyes for WNV (AOR = 2.8), and jaundice for DENV (AOR = 2.1) and WNV (AOR 2.8), although these associations were not statistically significant (Table 3).
Demographic, clinical and risk factors associated with anti-arboviral IgM seropositivity
The logistic regression analysis for IgM antibodies presents a different pattern of associations related to demographic, clinical, and risk factors influencing seropositivity across the surveyed population (Table 4). Gender emerged as a significant factor; females exhibited higher odds of IgM seropositivity across all tested viruses compared to males, with significant odds ratio observed for DENV (AOR = 2.8, 95% CI: 1.04-7.7, P <0.05) and CHIKV (AOR = 4.8, 95% CI: 1.5-15.8, P <0.01). Education level also played a pivotal role. A significant relationship was observed for DENV, with those having primary and secondary education levels showing lower odds of IgM seropositivity (AOR = 0.26, 95% CI: 0.08-0.87, P <0.05) and (AOR = 0.11, 95% CI: 0.02-0.64, P <0.01), respectively, compared to those with post-secondary education. Additionally, participants with secondary education had significantly lower odds of CHIKV IgM seropositivity (AOR = 0.06, 95% CI: 0.1-0.6, P <0.05) compared to those with post-secondary education. History of travel abroad was also linked to higher odds of IgM seropositivity across all viruses tested; however, the increase was not statistically significant. Notably, the odds of IgM seropositivity increased with some clinical characteristics such as bleeding for CHIKV (AOR = 2.0) and WNV (AOR = 8.1), body rashes for CHIKV (AOR = 1.5) and WNV (6.6), headache for DENV (AOR = 2.4) and CHIKV (AOR = 1.5), nausea for DENV (AOR = 5.0), red eyes for DENV (AOR = 1.3) and WNV (AOR = 1.2), stomach ache for DENV (AOR = 1.3), and jaundice for WNV (AOR = 7.8). Muscle pain for WNV (AOR = 6.9, 95% CI: 1.6-29.7, P <0.01) emerged as a significant clinical factor (Table 4).
Table 4.
Multivariate logistic regression model to predict anti-arboviral seropositivity (IgM) by social demographic, clinical and risk factors.
| Predictor variables | Levels | DENV (IgM) |
CKV (IgM) |
WN (IgM) |
|||
|---|---|---|---|---|---|---|---|
| AOR (95% CI) | P-value | AOR (95% CI) | P-value | AOR (95% CI) | P-value | ||
| Age group | <28 years | Ref | Ref | Ref | Ref | Ref | Ref |
| 28-42 | 1.9 (0.8-4.3) | 0.14 | 1.7 (0.66-4.4) | 0.28 | 1.51 (0.6-3.8) | 0.37 | |
| >42 | 2.02 (0.81-5.1) | 0.13 | 2.2 (0.81-5.9) | 0.12 | 1.96 (0.75-5.1) | 0.17 | |
| Sex | Male | Ref | Ref | Ref | Ref | Ref | Ref |
| Female | 2.8 (1.04-7.7) |
0.04* | 4.8(1.5-15.8) | 0.00* | 2.9(0.94-8.2) | 0.06 | |
| Education | College | Ref | Ref | Ref | Ref | Ref | Ref |
| None | 0.42 (0.1-1.7) | 0.22 | 0.35 (0.07-1.65) | 0.18 | 0.99 (0.22-4.42) | 0.99 | |
| Primary | 0.26 (0.08-0.87) | 0.03a | 0.28 (0.08-1.04) | 0.06 | 0.35 (0.08-1.42) | 0.14 | |
| Secondary | 0.11 (0.02-0.64) | 0.01a | 0.06 (0.0-0.6) | 0.02a | 0.29 (0.05-1.65) | 0.17 | |
| Occupation | Business | Ref | Ref | Ref | Ref | Ref | Ref |
| Farmer | 382,036 (0-1.0) | 0.97 | 476,910.1 (0-1.0) | 0.97 | 2.06 (0.24-17.5) | 0.50 | |
| Livestock | 1,098,208 (0-1.0) | 0.97 | 573,955.6 (0-1.0) | 0.97 | 2.3 (0.24-21.9) | 0.46 | |
| Other | 504,953 (0-1.0) | 0.97 | 584,504.9 (0-1.0) | 0.97 | 2.5 (0.28-21.7) | 0.42 | |
| Body temp (°C) | 37.4 | Ref | Ref | Ref | Ref | Ref | Ref |
| 37.5-38 | 0.49 (0.1-2.2) | 0.35 | 0.83 (0.22-3.03) | 0.78 | 0.87 (0.24-3.1) | 0.83 | |
| >38 | 0.62 (0.07-5.2) | 0.66 | 0 (0-1.0) | 0.98 | 0.87 (0.10-7.4) | 0.89 | |
| Place of living | Mutukula | Ref | Ref | Ref | Ref | Ref | Ref |
| Namanga | 1.7 (0.83-3.6) | 0.15 | 1.4 (0.59-3.1) | 0.47 | 1.1 (0.47-2.4) | 0.88 | |
| Clinical signs and symptoms | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Bleeding | 0 (0-1) | 0.97 | 2.0 (0.15-26.8) | 0.59 | 8.1 (0.43-151.9) | 0.16 | |
| Body malaise | 0.27 (0.03-2.5) | 0.25 | 1.03 (0.19-5.4) | 0.97 | 0.20 (0.01-3.2) | 0.25 | |
| Body rashes | 0.54 (0.06-4.7) | 0.58 | 1.5 (0.24-9.9) | 0.64 | 6.6 (0.94-46.4) | 0.06 | |
| Bruising | 0 (0-1.0) | 0.97 | 2.6 (0.23-27.8) | 0.44 | 0.0 (0-1.0) | 0.97 | |
| Headache | 2.4 (0.93-6.35) | 0.06 | 1.5 (0.5-4.5) | 0.43 | 0.13 (0.01-1.05) | 0.06 | |
| Muscle pain | 1.3 (0.29-5.6) | 0.75 | 1.6 (0.42-6.1) | 0.49 | 6.9 (1.6-29.7) | 0.01a | |
| Nause | 5.0 (0.88-28.3) | 0.06 | 0.41 (0.04-3.9) | 0.43 | 0.07 (0.00-3.6) | 0.19 | |
| Red eyes | 1.3 (0.12-12.8) | 0.85 | 0 (0-1.0) | 0.97 | 1.2 (0.04-38.8) | 0.92 | |
| Stomachache | 1.3 (0.38-4.6) | 0.66 | 0.45 (0.05-3.6) | 0.46 | 0.55 (0.04-8.0) | 0.66 | |
| Jaundice | 1.7 (0.13-20.7) | 0.69 | 0 (0-1.0) | 0.97 | 7.8 (0.22-273.7) | 0.26 | |
| Vomiting | 0 (0-1.0) | 0.97 | 1.1 (0.13-8.7) | 0.95 | 5.4 (0.15-191.4) | 0.35 | |
| History of travel abroad | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 1.2 (0.59-5.26) | 0.70 | 1.9 (0.63-5.5) | 0.26 | 1.5 (0.54-3.97) | 0.44 | |
| Mosquitoes in the surroundings | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.77 (0.38-1.6) | 0.48 | 1.5 (0.66-3.5) | 0.32 | 1.4 (0.60-3.0) | 0.45 | |
| Visiting forest | No | Ref | Ref | Ref | Ref | Ref | Ref |
| Yes | 0.95 (0.32-2.8) | 0.93 | 1.5 (0.66-3.5) | 0.32 | 1.4 (0.61-3.0) | 0.45 | |
AOR, adjusted odds ratio; CHIKV, Chikungunya virus; CI, confidence interval; DENV, dengue virus; Ig, immunoglobulin; WNV, West Nile virus.
P <0.05; Ref = reference group (AOR = 1).
Discussion
CHIKV, DENV, and WNV viruses are closely related mosquito-borne pathogens with similar transmission cycles and clinical manifestations. This study reveals significant prevalence of all three viruses across the surveyed borders. While CHIKV and DENV align with their known endemicity in Tanzania, the detection of WNV is previously undocumented in Tanzania, as no prior outbreaks have been reported [19].
The seroprevalence rates are consistent with findings from sub-Saharan Africa, particularly in border communities in Tanzania, Uganda, and Kenya, highlighting risks from increased mobility and vector exposure [[3], [4], [5]]. Co-infections involving DENV and CHIKV underscore the complexity of arbovirus transmission where Aedes and Culex vectors coexist, aligning with other studies [6]. The absence of WNV co-infections may reflect diagnostic limitations, such as ELISA cross-reactivity among flaviviruses [20]. The introduction and establishment of these viruses in cross-border buffer zones may be attributed to the geographic expansion of human and mosquito vectors due to increased travel and trade. Studies link such mobility to arbovirus spread across previously unaffected regions [7,8].
The higher seroprevalence at Namanga compared to Mutukula suggests localized factors influencing transmission dynamics. Environmental conditions at Namanga, potentially more conducive to mosquito breeding due to higher rainfall or warmer temperatures, may increase vector populations and transmission rates. Namanga’s role as a major transit point likely exacerbates these conditions by facilitating human and vector mobility, introducing new virus strains, and increasing transmission opportunities. These dynamics align with the complexity of arboviral transmission and the influence of ecological and human factors [10].
The association between gender and seropositivity, with higher prevalence among females, suggests exposure to infected mosquitoes influenced by gender-specific behaviors. Women’s activities, such as water collection, market trading, or agricultural work, often occur in mosquito-prone areas, increasing vector contact, particularly during evening hours, as supported by studies on arboviral risk in similar settings [[11], [12], [13]].
Age-related trends, with higher seroprevalence in the 28-42 age group, may reflect lifestyle and occupational exposures. Younger adults, often mobile and engaged in outdoor employment or recreation, face greater mosquito exposure. This age group dominates sectors like agriculture and informal markets, which lack protective measures against bites [[14], [15], [16]].
Higher educational attainment correlated with increased seroprevalence, particularly for CHIKV and DENV, possibly due to occupational travel or assignments in high-risk areas [17,18]. Non-formal employment and crop farming showed elevated WNV seroprevalence, likely due to unprotected work environments. Crop farmers, active during peak mosquito hours, face higher vector contact than those in indoor settings [21,22]. Geographical disparities, with Namanga’s higher seroprevalence, reflect local ecological conditions (e.g., breeding sites, climate) and varying public health infrastructure [10,12]. This interplay of demographic, occupational, educational, and geographical factors highlights the multifaceted nature of arboviral transmission and the need for tailored public health strategies.
This study acknowledges limitations, including its cross-sectional design, which limits causal inference and infection timing determination, and the absence of plaque reduction neutralization tests, which are critical for confirming ELISA results due to flavivirus cross-reactivity [20]. Recall bias in self-reported travel or mosquito exposure may affect estimates. Despite these limitations, this study provides critical data to shape public health policies in cross-border regions. We recommend enhancing surveillance with comprehensive outbreak detection systems; strengthening healthcare infrastructure for early diagnosis at border crossings; implementing community education on arbovirus symptoms and early intervention; intensifying vector control, including insecticides and breeding site elimination; and fostering cross-border collaboration to monitor and control arbovirus spread, reducing incidence and impact.
Conclusion
This study confirms ongoing DENV and CHIKV circulation and provides the first evidence of WNV exposure in these cross-border areas of Tanzania. These findings indicate widespread arbovirus exposure, necessitating enhanced surveillance and vector control. Improved differential diagnosis of febrile syndromes via multiplex polymerase chain reaction can enhance clinical outcomes. Expanding longitudinal serosurveys and vector studies to other border regions, integrating migration and trade data, could further elucidate transmission dynamics, informing Tanzania’s and EAC’s health strategies.
Declaration of competing interest
The authors have no competing interests to declare.
Acknowledgments
Funding
Tanzania Ministry of Health, through a cooperative agreement with the United States of America, Global Health Security project fund.
Ethical approval
This study received ethical approval from the Tanzania Medical Research Coordinating Committee of the National Institute for Medical Research. Permission to conduct the study in the selected regions and districts was sought from the respective regional and district authorities. Written informed consent was sought from study participants prior to their involvement.
Acknowledgments
We extend our sincere gratitude to the Ministry of Health staff, the National Public Health Laboratory, and the Region and District administrative Authorities for allowing and supervising this research work. More specifically, special thanks is extended to Josephine Kapinga (Logistic coordinator), Dr. Amour Seleman (Head of port health services), Athanas Lucas, Nisalile Mwangoka from the Ministry of Health Headquarters; Francis Moreen and Raphael Mwakasege (GHSA), Medard Beyanga (Former Director) from the National Public Health Laboratory; Dr. Kaniki Issessanda (Former RMO-Kagera region), Vones Uiso (RHO-Arusha region), Dr. Daniel Chochole (DMO-Misenyi District), and Remmy Kibiki and Gabriel Mdachi, Port Health Officers in charge at Namanga and Mutukula borders.
Author contributions
R.K drafted the manuscript. R.K, F.N, S.R, A.M, KM, EGK and EM jointly reviewed and updated the draft manuscript. All authors checked and approved the final report.
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