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. 2026 Jul 8;23:233. doi: 10.1186/s12985-026-03233-y

Spatiotemporal dynamics of Zika in Brazil, 2016–2023: a nationwide population-based study

Marcos César de Souza Santos 1, Paula Esbaltar de Oliveira 1, Phoebe Silva van Emmerik 1, Rafael Pedro de Souza Nascimento 1, Vanessa Cardoso Pereira 1, Sávio Luiz Pereira Nunes 2, Havandécio Rodrigues de Matos Júnior 1, Chirles Araújo de França 1, Adeilton Gonçalves da Silva Junior 1, Thais Silva Matos 1, Marcio Bezerra Santos 3, Carla Pacheco Teixeira 4, Diana Paola Gutierrez Diaz de Azevedo 4, Daniel Elías Cuartas Arroyave 5, José Valter Joaquim Silva Júnior 6, Rodrigo Feliciano Carmo 1, Carlos Dornels Freire de Souza 1,✉
PMCID: PMC13628813  PMID: 42421045

Abstract

Introduction

The Zika epidemic was first reported in Brazil in 2015. Since then, several factors have sustained the virus transmission chain in the country. This study aimed to describe the epidemiological profile and temporal trends, as well as to identify significant spatial and spatiotemporal clusters of Zika in Brazil between 2016 and 2023.

Methods

Ecological study including confirmed Zika cases in Brazil from 2016 to 2023. Data were extracted from the National Notifiable Diseases Information System. Sociodemographic variables (age, sex, race/ethnicity, education level, and pregnancy status) were collected, along with incidence rates. Analyses were conducted in three steps: (1) description of the epidemiological profile; (2) temporal analysis: Joinpoint regression modeling was applied to identify trends and the Average Annual Percent Change (AAPC) was calculated with 95% confidence intervals and 5% significance level; (3) spatial analysis: spatial dependence among municipalities was assessed using the Global Moran’s I index, followed by spatial scan statistics to identify spatial clusters of virus transmission, using both purely spatial and spatiotemporal analyses.

Results

A total of 176,122 cases were reported during the study period. Among them, 42.27% (n = 74,458) were aged 20–39 years, 66.96% (n = 117,945) were female, and 34.92% (n = 61,513) were mixed-race. A decreasing trend in incidence rates was observed in Brazil (AAPC − 36.55%; p < 0.001), with disparities across states. Fifteen out of 27 states showed a downward trend. Rio Grande do Norte was the only state to exhibit an increasing incidence trend and was also identified as the area with the highest relative risk of Zika infection in the country (RR = 12.1; p < 0.001). In the spatio-temporal analysis, the cluster with the highest relative risk (RR 31.08; p < 0.001) included 2,770 municipalities across the North, Northeast, Southeast, and Central-West regions.

Conclusion

Zika cases predominated among adults, females, and mixed-race individuals. In Brazil, the overall incidence rate showed a decreasing trend during the study period. The Northeast and Central-West regions had the largest number of clusters, highlighting the need to strengthen surveillance activities in these areas to reduce transmission rates.

Keywords: Zika virus, Joinpoint regression, Global Moran’s I index, Epidemiology

Introduction

Zika is an arboviral disease caused by a virus of the Orthoflavivirus genus, family Flaviviridae. Transmission occurs mainly through the bite of an infected mosquito of the Aedes genus [1]. In addition to the vector, other transmission routes have been described, such as sexual and vertical transmission from mother to fetus [2]. Infection is usually asymptomatic, but it may produce a clinical presentation similar to other arboviral diseases, such as dengue and chikungunya, with arthralgia, maculopapular rash, fever, headache, and myalgia [3, 4].

The virus was first discovered in Uganda in 1947, being isolated from a non-human primate [4]. The first reports of infection in the Americas and the Caribbean were documented in 2013 [3, 5]. In Brazil, the first autochthonous cases were reported in the states of Bahia and Rio Grande do Norte in 2015 [6], although it is believed that the virus was introduced around 2013 [7]. Between 2015 and 2016, a Zika epidemic occurred in South America, when the suspected association between Zika Virus (ZIKV) infection and congenital Zika syndrome was raised [4].

Epidemiological records show that the virus has spread to all continents. By 2016, more than 52 nations had reported autochthonous transmission within their territories [8]. In 2023, the number of countries with reported Zika cases rose to 92, mainly in Africa, Latin America, and Southeast Asia [6]. In the same year, 55,813 cases of Zika were reported in the Americas, and 97% of notifications came from Brazil [9].

In Brazil, several measures were adopted to control ZIKV transmission and its consequences. From 2015 to 2018, the main actions established by legislation focused on vector control (Aedes aegypti) [10]. Decree No. 8,612, issued on December 21, 2015, for instance, was created to monitor and coordinate actions to address arboviral diseases [11]. In 2016, approximately R$1.5 billion were invested nationwide to combat the mosquito [12].

Despite these efforts, the virus became endemic in Brazil. Between 2016 and 2020, 162,462 cases of ZIKV infection were confirmed [13]. The Northeast and Southeast regions accounted for most reported cases, particularly in the states of Rio de Janeiro and Bahia [14, 15]. Several factors contribute to ZIKV transmission in the country, including high temperatures, high annual rainfall, inadequate sanitation, improper disposal of urban waste, and poverty [16].

This study arises from the need to identify the spatiotemporal patterns of Zika occurrence in Brazil, considering the analytical potential of time series models and spatial analysis techniques to better understand the dynamics of disease transmission. The knowledge generated from this investigation may support timely public health decision-making by identifying significant spatial and spatiotemporal clusters of Zika transmission, thereby facilitating targeted surveillance and control measures.

Therefore, this study aimed to describe the epidemiological profile and temporal trends, as well as to identify significant spatial and spatiotemporal clusters of Zika in Brazil between 2016 and 2023.

Methods

Study design, population, and period

This is an ecological study involving all confirmed cases of Zika reported between 2016 and 2023 in Brazil. A suspected case of Zika is defined as any patient presenting with pruritic maculopapular rash accompanied by fever, or conjunctival hyperemia, or arthralgia, or periarticular edema. A confirmed case, in turn, is any suspected case confirmed by at least one of the following criteria: laboratory or clinical-epidemiological [17].

Study setting

The study covered Brazil, its five macro-regions (North, Northeast, South, Southeast, and Central-West), and the 27 federative units (Fig. 1). The country has continental dimensions, with an area of 8.5 million square kilometers. It also has a population of approximately 203 million inhabitants living in 5,570 municipalities [18].

Fig. 1.

Fig. 1

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Geographic distribution of Brazil by macro-regions

Data sources and study variables

Secondary data of publicly accessible were retrieved from the Sistema Nacional de Agravos de Notificação (SINAN, National Notifiable Diseases Information System), accessed through the database of the Department of Informatics of the Unified Health System (http://tabnet.datasus.gov.br/) [19]. Demographic data were collected from the Brazilian Institute of Geography and Statistics (https://sidra.ibge.gov.br/home/pnadct/brasil). Data collection was performed on August 13, 2024.

The sociodemographic variables analyzed were: year of diagnosis; place of residence (region, state, and municipality); age group (< 1 year, 1–4, 5–9, 10–14, 15–19, 20–39, 40–59, ≥ 60 years); sex; race/ethnicity (White, Black, Yellow, Mixed-race, and Indigenous) used by the Brazilian Institute of Geography and Statistics; education level (illiterate, primary education, secondary, higher education); and pregnancy status. In addition, the incidence rate (IR) was calculated: number of Zika cases/resident population × 100,000.

Study steps

  1. Descriptive analysis of sociodemographic and clinical profile: Sociodemographic variables were expressed as absolute and relative frequencies (%).

  2. Time series analysis: The Joinpoint regression model was applied to identify temporal trends in confirmed Zika cases. This model aims to assess whether a line composed of multiple segments provides a better statistical fit to represent the temporal evolution of the data compared to a straight line or models with fewer segments [20].

The model allows the evaluation of trend direction (stationary, increasing, or decreasing), as well as the identification of joinpoints where trend changes occur. Trends were classified as increasing (⬆) when APC/AAPC values were positive and statistically significant (p < 0.05), decreasing (⬇) when values were negative and statistically significant, and stationary (⬌) when no statistically significant change was identified.

This approach estimates both the Annual Percent Change (APC) and the Average Annual Percent Change (AAPC) throughout the study period. Considering the time series (2016–2023), the analysis was restricted to a minimum of 0 and a maximum of 2 joinpoints to avoid model overfitting. The final number of joinpoints was determined through permutation tests performed by the software. Model selection was based on 4,499 permutations, adopting a significance level of 5%, 95% confidence intervals, and error autocorrelation based on observed data. Analyses were conducted using the Joinpoint Regression Program, version 5.2.0 (National Cancer Institute, Bethesda, MD, USA), covering three spatial units: Brazil, its five macro-regions, and the 27 federative units.

  • 3)

    Spatial analysis: Spatial autocorrelation was first assessed using Global Moran’s I index to identify the presence of spatial dependence among municipalities. This statistic provides a measure of overall spatial autocorrelation across the study area, indicating whether municipalities with similar incidence rates tend to be geographically clustered. However, it does not identify the specific locations responsible for the observed spatial pattern. After confirmation of global autocorrelation, Local Moran’s I index (Local Indicators of Spatial Association – LISA) was applied to identify localized spatial association patterns. Unlike Global Moran’s I, LISA identifies the municipalities contributing to the global spatial dependence and classifies local patterns of spatial association, allowing the identification of priority areas for public health interventions. For this analysis, first-order neighbors (i.e., municipalities sharing borders) were considered, with all adjacent objects assigned equal weight [21].

Based on the LISA analysis, municipalities were classified into quadrants of Moran’s scatterplot according to the relationship between their attribute value and the average value of their neighbors. The quadrants were defined as follows: Q1 (high-high), municipalities with attribute values above the overall average, as well as their neighbors; Q2 (low-low), municipalities with values below the overall average, along with their neighbors; Q3 (high-low), municipalities with above-average values surrounded by neighbors with below-average values; and Q4 (low-high), municipalities with below-average values surrounded by neighbors with above-average values. The areas located in Q1 are considered priorities for intervention [21].

Subsequently, spatial scan statistics were applied to identify significant spatial and spatiotemporal clusters of ZIKV transmission among municipalities. While Moran’s statistics evaluate spatial dependence based on similarity between neighboring municipalities, the spatial scan statistic is a model-based approach that identifies statistically significant clusters with higher-than-expected disease occurrence relative to the underlying population at risk. This technique is based on the discrete Poisson probability model and uses the maximum likelihood test to detect spatio-temporal clusters under the alternative hypothesis that the risk within the scanning window is significantly higher than outside it [22]. A maximum scanning radius of 500 km was defined a priori to enable the detection of clusters at broader regional scales while preserving sensitivity to local aggregation. Combined with a maximum population-at-risk of 50%, this parameterization reduces the likelihood that excessively large windows mask meaningful spatial heterogeneity.

Two analyses were performed: retrospective purely spatial and retrospective spatio-temporal. The purely spatial analysis sets a circular window on the map, centered at one of the many grid points positioned throughout the study area. This method allows the creation of an infinite number of distinct geographic circles, with different sets of neighboring data locations within them, each circle representing a potential cluster candidate [22].

The spatio-temporal scan is operationalized using a cylindrical window, where the base is a geographic circular (or elliptical) area and the height represents the time period analyzed. The base of the cylinder corresponds to the purely spatial analysis, while the height incorporates the temporal dimension, reflecting the duration of possible clusters. This cylindrical window moves continuously across space and time, generating an infinite number of overlapping cylinders with different shapes and sizes, each representing a potential spatiotemporal cluster [21].

To determine the statistical significance of clusters, Monte Carlo simulations with 999 permutations were performed, and clusters with p < 0.05 were considered significant. Clusters consisting of a single municipality were excluded, as the spatial scan statistic is designed to identify aggregations of neighboring geographic units; isolated municipalities do not represent true spatial clustering and may lead to misinterpretation of localized variation. For this stage, the following software was used: QGIS (version 3.38.2, Open Source Geospatial Foundation, Beaverton, OR, USA), GeoDa (version 1.22.0.4, Spatial Analysis Laboratory, Anselin, USA), and SaTScan (version 10.2.4, National Cancer Institute, Bethesda, MD, USA).

Ethical aspects

The study did not require approval by a Research Ethics Committee, as it used secondary data, in accordance with Resolution 510/2016 of the Brazilian National Research Ethics Commission (CEP/CONEP).

Results

Between 2016 and 2023, a total of 176,122 cases of Zika were reported in Brazil, corresponding to an average of 22,015 cases per year. Among these, women predominated, accounting for 66.96% (n = 117,945; IR 14.06/100,000) of cases, and adults aged 20 to 39 years represented 42.27% (n = 74,458; IR 13.92/100,000). The population self-identified as mixed-race also stood out, corresponding to 34.92% (n = 61,513) of reported cases. Educational level was recorded in only 33.2% (n = 58,516) of notifications. The proportion of individuals with recorded gestational age was 8.29% (n = 14,613) of cases. Additionally, the incidence rate was higher among females (IR 14.06/100,000) and in the age group under 1 year (IR 17.41/100,000). Most confirmed cases were classified using clinical-epidemiological criteria (83.1%; n = 146,265), whereas 16.9% (n = 29,857) were laboratory-confirmed (Table 1).

Table 1.

– Epidemiological characterization of confirmed reported cases of Zika. Brazil, 2016–2023. (n = 176,122)

Variable % (n) IR/100,000
*Sex
Male 32.86 (57883) 7.03
Female 66.96 (117945) 14.06
**Age group
< 1 year 2.17 (3833) 17.41
1–4 years 3.59 (6337) 6.92
5–9 years 4.57 (8066) 7.00
10–14 years 5.52 (9728) 8.24
15–19 years 8.38 (14763) 11.66
20–39 years 42.27 (74458) 13.92
40–59 years 24.87 (43812) 10.48
≥ 60 years 8.55 (15068) 6.38
Race/Ethnicity
Not recorded 39.88 (70247) -
White 20.53 (36171) -
Black 3.75 (6617) -
Yellow 0.6 (1061) -
Mixed-race 34.92 (61513) -
Indigenous 0.29 (513) -
Educational level
Illiterate 0.37 (654) -
Primary education 12.76 (22486) -
Secondary education 14.82 (26114) -
Higher education 5.25 (9262) -
Not recorded/“Not applicable” 66.77 (117606) -
Pregnancy status
1 st trimester 2.07 (3656) -
2nd trimester 3.34 (5884) -
3rd trimester 2.88 (5073) -
Gestational age not recorded 0.30 (542) -
No 32.29 (56880) -
Not recorded/“Not applicable” 59.09 (104087) -
Confirmation criterion
Clinical-epidemiological 83.1 (146,265)
Laboratory 16.9 (29,857)
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0.16% (n = 294) of records had sex not recorded. 0.032% (n = 57) of records had age left blank. IR = Incidence Rate. (-) Population denominator estimates were unavailable for these variables

A decline in the rate of confirmed Zika cases was observed in Brazil, decreasing from 69.10/100,000 in 2016 to 1.51/100,000 in 2023, with an overall downward trend (AAPC − 36.55%; p < 0.001). Three regions showed a decreasing trend: (i) the Central-West region, where the rate dropped from 168.70/100,000 in 2016 to 4.09/100,000 in 2023 (AAPC: −42.76%; CI: −61.67 to − 14.52; p = 0.006); (ii) the Southeast region, where the rate decreased from 79.02/100,000 in 2016 to 0.07/100,000 in 2023 (AAPC: −61.58%; CI: −71.22 to − 48.97; p < 0.001); and (iii) the North region, with a decline from 58.32/100,000 in 2016 to 3.34/100,000 in 2023 (AAPC: −32.79%; CI: −40.87 to − 23.61; p < 0.001). A stationary trend was observed in the Northeast and South regions (Fig. 2).

Fig. 2.

Fig. 2

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Temporal trend of Zika incidence rates in Brazil and regions, 2016–2023. Legend: Trend direction (Zero joinpoints indicate a single linear trend across the study period; one joinpoint indicates two distinct trend segments separated by a statistically significant change point). AAPC: Average Annual Percent Change. APC: Annual Percent Change. IC: Confidence Intervals. Statistical significance (p < 0,05)

Over the study period, 15 of the 27 federative units showed a declining trend, including three in the North region, three in the Northeast, four in the Central-West, four in the Southeast, and one in the South. Although the rate in Rio Grande do Norte was lower in the final year of the study period (5.69/100,000) was lower than in the first year (6.14/100,000), it exhibited successive increases between 2017 and 2020 (rates of 0.49/100,000, 1.98/100,000, 2.91/100,000, and 8.69/100,000, respectively). The rate also rose from 6.21/100,000 in 2021 to 21.07/100,000 in 2022 in this state. All other federative units showed stationary trends (Table 2).

Table 2.

Temporal trend of Zika incidence rates by regions and federative units. Brazil, 2016–2023

Spatial unit Rate1 AAPC (95% CI) p-value Trend
2016 2023
Observed (crude) Modeled(adjusted) Observed (crude) Modeled(adjusted)
North 58.32 47.62 3.34 2.95 −32.79%* (−40.87 to −23.61; p < 0.001) ⬇
RO 33.18 8.35 2.53 1.71 −20.29% (−37.32 to 1.37; p = 0.060) ⬌
AC 10.66 4.09 12.17 4.09 −0.0038% (−24.46 to 32.38; p = 0.999) ⬌
AM 107.18 73.26 5.61 6.60 −29.10% (−48.36 to −2.65; p = 0.034) ⬇
RR 31.75 61.32 8.95 2.49 −36.74% (−57.77 to −5.23; p = 0.026) ⬇
PA 36.75 6.60 0.18 0.18 −40.22% (−54.64 to −21.22; p = 0.004) ⬇
AP 49.47 2.94 2.18 1.29 −11.09% (−46.17 to 46.85; p = 0.587) ⬌
TO 118.25 73.79 8.60 12.10 −22.76% (−51.71 to 23.55; p = 0.281) ⬌
Northeast 64.58 34.36 3.18 5.78 −22.49% (−47.39 to 14.22; p = 0.198) ⬌
MA 58.49 54.69 1.56 1.00 −43.58% (−67.58 to −1.83; p = 0.043) ⬇
PI 1.94 1.38 0.76 0.70 −9.34% (−20.56 to 3.46; p = 0.119) ⬌
CE 26.11 10.02 0.10 0.14 −45.45% (−68.10 to −6.72; p = 0.033) ⬇
RN 6.14 1.10 5.69 15.55 46.06% (19.52 to 78.51; p = 0.004) ⬆
PB 18.11 5.47 2.49 8.86 7.13% (−18.46 to 40.74; p = 0.560) ⬌
PE 1.91 0.67 0.03 0.20 −16.06% (−41.92 to 21.31; p = 0.289) ⬌
AL 119.40 15.32 1.92 4.48 −16.10% (−39.10 to 15.59; p = 0.229) ⬌
SE 1.61 0.66 9.91 11.04 49.68% (−11.24 to 152.42; p = 0.108) ⬌
BA 168.20 89.60 7.27 7.83 −29.41% (−39.71 to −17.35; p < 0.001) ⬇
Central-West 168.70 111.41 4.09 2.24 −42.76% (−61.67 to −14.52; p = 0.006) ⬇
MS 65.09 8.61 2.94 0.61 −31.44% (−47.32 to −10.77; p = 0.013) ⬇
MT 489.96 419.69 12.74 6.20 −45.23 (−67.33 to −8.17; p = 0.022) ⬇
GO 119.50 85.81 1.69 1.80 −42.42% (−56.43 to −23.92; p < 0.001) ⬇
DF 6.43 5.03 0.00*** 0.01 −57.98% (−72.49 to −35.81; p = 0.002) ⬇
Southeast 79.02 22.56 0.07 0.03 −61.58% (−71.22 to −48.97; p < 0.001) ⬇
MG 46.03 8.52 0.26 0.08 −49.02% (−67.29 to −20.53; p = 0.010) ⬇
ES 59.60 158.36 < 0.00** < 0.00 −83.27% (−92.62 to −62.08; p = 0.002) ⬇
RJ 305.91 120.11 0.01 0.02 −70.97% (−76.47 to −64.20; p < 0.001) ⬇
SP 10.11 1.83 0.01 0.01 −55.91% (−64.13 to −45.81; p < 0.001) ⬇
South 1.79 0.12 0.04 0.08 −6.65% (−45.33 to 59.41; p = 0.764) ⬌
PR 3.49 0.44 0.00*** 0.00 −51.24 (−66.61 to −28.79; p = 0.004) ⬇
SC 0.70 0.33 0.00*** 0.00 −45.13 (−76.73 to 29.36; p = 0.170) ⬌
RS 0.77 0.07 0.11 0.25 20.22% (−34.33 to 120.08; p = 0.484) ⬌
Brazil 69.10 40.78 1.51 1.68 −36.55% (−44.57 to −27.36; p < 0.001) ⬇
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*Statistical significance (p < 0.05); 1. Rate per 100,000 inhabitants; AAPC: Average Annual Percent Change; ⬆ = Increasing trend; ⬇ = Decreasing trend; ⬌ = Stationary trend; RO: Rondônia; AC: Acre; AM: Amazonas; RR: Roraima; PA: Pará; AP: Amapá; TO: Tocantins; MA: Maranhão; PI: Piauí; CE: Ceará; RN: Rio Grande do Norte; PB: Paraíba; PE: Pernambuco; AL: Alagoas; SE: Sergipe; BA: Bahia; MG: Minas Gerais; ES: Espírito Santo; RJ: Rio de Janeiro; SP: São Paulo; PR: Paraná; SC: Santa Catarina; RS: Rio Grande do Sul; MS: Mato Grosso do Sul; MT: Mato Grosso; GO: Goiás; and DF: Distrito Federal. From 2020 onwards, the state of Espírito Santo adopted the e-SUS Health Surveillance System. **Therefore, for cases of urban arboviral diseases in Espírito Santo, only data available from Sinan Online (dengue and chikungunya) and Sinan Net (Zika) were considered. ***No cases in 2023

Considering the incidence rate for the period 2016–2023, 4.09% (n = 228) of Brazilian municipalities presented a rate > 2/100,000 inhabitants. Clusters with high incidence rates were observed in all regions, although a concentration was noted in the Central-West region of Brazil (Fig. 3A).

Fig. 3.

Fig. 3

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Spatial distribution of Zika incidence rates in Brazil and Moran Map, 2016–2023

In the Moran map, 3.34% (n = 186) of municipalities were located in the High-High quadrant (Q1). These municipalities belonged to 12 Brazilian states: two in the North region (Pará and Tocantins), two in the Southeast (Minas Gerais and Rio de Janeiro), three in the Central-West (Goiás, Mato Grosso do Sul, and Mato Grosso), and five in the Northeast (Alagoas, Bahia, Maranhão, Paraíba, and Rio Grande do Norte). Together, they recorded a total of 52,068 cases throughout the study period, corresponding to 29.56% of all cases (Fig. 3B).

Figure 3 presents the cumulative spatial distribution of incidence rates for the entire study period, whereas. Figure 4 illustrates the annual spatial autocorrelation patterns. Therefore, regions with low or no cumulative incidence may still present significant spatial clusters in specific years, particularly in 2022 and 2023, reflecting temporal variations in disease occurrence and spatial dependence over time.

Fig. 4.

Fig. 4

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Annual Moran Map for Zika incidence rates in Brazil, 2016–2023

The local Moran’s I statistic showed spatial dependence in all analyzed years. Spatial clusters were identified, with a predominant concentration in the Central-West region. The number of municipalities in the Q1 (High-High) quadrant varied across the years, ranging from 139 (2.49%) in 2016 to 70 (1.25%) in 2018. The greatest increase was observed between 2021 and 2022, with a 36.14% rise in the number of municipalities in the Q1 quadrant. In contrast, the largest reduction occurred after the first year, with a 43.88% decrease in the number of priority municipalities from 2016 to 2017 (Fig. 4).

In 2016, 139 municipalities were located in the Q1 quadrant, with the highest concentration in Mato Grosso (53.95%). This state maintained high numbers in the following years, ranking as the second state with the largest number of high-risk municipalities in 2017 (20.51%), behind only Tocantins (21.79%), and as the leading state in 2018 (32.85%). From 2019 to 2023, Mato Grosso continued to register priority municipalities, but northeastern states stood out, particularly Alagoas, Bahia, Paraíba, and Rio Grande do Norte (Fig. 4).

The purely spatial analysis identified 23 clusters, with the cluster showing the highest relative risk (RR = 12.1) located in the state of Rio Grande do Norte, within a radius of 17 km, encompassing three municipalities and 112 cases (p < 0.001) (Table 3; Fig. 5).

Table 3.

Retrospective purely spatial and space-time analyses of Zika incidence in Brazil, 2016–2023

Cluster* States Radius (Km) Number of Municipalities Number of cases Annual Rate/100,000 Relative Risk p-value
Analysis 1- Purely spatial
1 Espírito Santo and Minas Gerais. 137.4 58 53,990 47.3 6.2 < 0.001
2 Mato Grosso do Sul, Mato Grosso, Goiás, Tocantins and Pará. 614.5 301 28,736 49.5 5.5 < 0.001
3 Espírito Santo and Minas Gerais. 118.4 49 16,696 49.5 5.2 < 0.001
4 Minas Gerais. 27.5 8 3978 108.3 10.7 < 0.001
5 Alagoas. 34.0 14 4054 36.7 3.6 < 0.001
6 Maranhão. 18.0 2 3486 34.0 3.3 < 0.001
7 Tocantins. 68.6 8 1480 46.5 4.5 < 0.001
8 Bahia and Minas Gerais. 146.7 46 1978 30.4 3.0 < 0.001
9 Paraíba. 16.6 7 572 60.6 5.9 < 0.001
10 Tocantins and Pará. 127.0 18 993 28.1 2.7 < 0.001
11 Ceará. 15.6 2 193 106.9 10.4 < 0.001
12 Minas gerais. 15.7 3 401 35.9 3.5 < 0.001
13 Rio Grande do Norte. 17.0 3 112 124.8 12.1 < 0.001
14 Rio de Janeiro. 3.4 2 628 23.2 2.3 < 0.001
15 Minas Gerais. 30.6 3 118 77.0 2.5 < 0.001
16 Ceará. 51.8 3 307 29.5 2.9 < 0.001
17 São Paulo. 38.2 10 1153 16.4 1.6 < 0.001
18 Bahia. 35.3 7 242 29.2 2.8 < 0.001
19 Rio Grande do Norte and Paraíba. 18.7 6 200 29.7 2.9 < 0.001
20 Pernambuco and Paraíba. 29.0 4 133 38.5 3.7 < 0.001
21 Minas Gerais. 62.2 7 184 26.0 2.5 < 0.001
22 Ceará. 30.8 5 188 21.4 2.1 < 0.001
23 Rio Grande do Norte. 29.7 4 75 34.6 3.4 < 0.001
Analysis 2- Retrospective space-time
1 Bahia, Sergipe, Alagoas, Pernambuco, Ceará, Piauí, Maranhão, Espírito Santo, Rio de Janeiro, São Paulo, Minas Gerais, Mato Grosso do Sul, Mato Grosso, Goiás, Distrito Federal, Tocantins and Pará. 1056.90 2016 2770 113,470 118.3 31.08 < 0.001
2 Pernambuco and Alagoas. 29.66 2022 12 278 101.3 9.81 < 0.001
3 Acre and Amazonas. 201.62 2021 8 263 106.0 10.27 < 0.001
4 Rio Grande do Norte and Paraíba. 518.70 2021 to 2022 171 1730 17.8 1.73 < 0.001
5 Pernambuco and Paraíba. 18.77 2018 to 2021 3 117 74.9 7.25 < 0.001
6 Pernambuco and Alagoas. 17.42 2019 to 2022 5 127 62.4 6.04 < 0.001
7 Mato Grosso. 86.98 2020 8 97 76.5 7.40 < 0.001
8 Mato Grosso. 153.38 2018 19 310 27.2 2.64 < 0.001
9 Goiás. 115.60 2018 16 120 55.0 5.32 < 0.001
10 Mato Grosso. 93.18 2021 3 45 259.6 25.13 < 0.001
11 Espírito Santo. 135.19 2019 4 355 24.1 2.33 < 0.001
12 Tocantins. 58.25 2018 to 2019 4 92 66.7 6.46 < 0.001
13 Bahia. 42.75 2019 to 2021 7 149 38.7 3.74 < 0.001
14 Mato Grosso. 93.15 2020 5 90 54.5 5.27 < 0.001
15 Bahia. 29.04 2020 3 48 104.1 10.08 < 0.001
16 Alagoas. 21.73 2021 5 67 58.5 5.66 < 0.001
17 Bahia. 11.60 2022 2 32 125.0 12.09 < 0.001
18 Alagoas. 8.50 2021 3 23 170.9 16.53 < 0.001
19 Alagoas. 14.01 2021 2 31 100.2 9.70 < 0.001
20 Minas Gerais. 68.94 2019 12 72 31.6 3.06 < 0.001
21 Goiás. 15.24 2018 5 32 48.8 4.72 < 0.001
22 Paraíba. 16.15 2018 4 104 22.3 2.15 < 0.001
23 Paraíba. 12.99 2019 3 157 18.4 1.78 < 0.001
24 Alagoas. 42.24 2019 18 213 16.5 1.60 < 0.001
Open in a new tab

In the tables, non-significant clusters (p > 0.05), single-municipality clusters, and clusters with RR < 1 were excluded. Therefore, the cluster numbering may vary

Fig. 5.

Fig. 5

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Retrospective purely spatial and space-time analyses of Zika incidence in Brazil, 2016–2023

Regarding the retrospective space-time analysis, 24 clusters were detected. The cluster with the largest radius (1056.90 km), the highest number of municipalities (2770), and the highest relative risk (RR = 31.08) was located in the states of Bahia, Sergipe, Alagoas, Pernambuco, Ceará, Piauí, Maranhão, Espírito Santo, Rio de Janeiro, São Paulo, Minas Gerais, Mato Grosso do Sul, Mato Grosso, Goiás, Federal District, Tocantins, and Pará during the year 2016 (p < 0.001). The second highest space-time cluster (RR = 25.13) was located in Mato Grosso, concentrating 45 cases across three municipalities in 2021 (Table 3; Fig. 5).

Discussion

This study described the epidemiological profile and temporal trends of Zika and identified spatial clusters of the disease in Brazil between 2016 and 2023. The epidemiological profile was predominantly characterized by women, the economically active population, and individuals of mixed-race/ethnicity. Temporal analysis demonstrated a decreasing trend in incidence rates in Brazil, with disparities among the federative units. The Central-West and Northeast regions stood out for having a greater number of municipalities within high-risk clusters, as well as clusters with higher relative risks.

Findings related to the population profile are consistent with data from other national and international studies [6, 23–25]. The predominance of females and young people was demonstrated in different regions of Brazil, such as Piauí [23], Rio Grande do Norte [26], Goiás [27] and Minas Gerais [28]. Similar results were also observed in Colombia, where 62.4% of individuals affected by ZIKV from 2014 to 2016 were women, with a mean age of approximately 41.6 years [24].

The predominance of infected females may be related to greater exposure of this population, considering that mosquito proliferation occurs mainly in peri-domestic areas and that domestic activities, still largely performed by women, expose them to infection [25]. In São Paulo, a study showed that 89.5% of eggs collected in containers inside residences belonged to Aedes aegypti [29].

It has also been pointed out that females seek medical care more frequently than males [30], which may contribute to higher diagnosis rates. The 2019 National Health Survey revealed that 80.6% of women and 66.6% of men had consulted a physician in the previous 12 months in Brazil, showing that females use health services more often. Furthermore, sexual transmission may contribute to the higher proportion of reported cases among women. A systematic review found that sexual transmission of ZIKV occurs predominantly from men to women and that ZIKV RNA persists longer in semen (median: 34 days) than in the female genital tract (median: 12 days), potentially increasing the opportunity for male-to-female transmission [31]. Specifically in the Brazilian context, in light of the ZIKV-associated Congenital Syndrome, protocols were adopted to implement actions focused on the female population, which may have favored seeking healthcare services [25].

The predominance of mixed-race populations observed (34%) is similar to findings from other studies [6, 23, 32]. This percentage may be even higher when considering the proportion of missing data in this variable (39.88%). Besides being the predominant population in Brazil, black race/ethnicity is subject to higher social vulnerability. Extreme poverty rates are higher among black (4.7%) and mixed-race (6.0%) individuals compared to White individuals (2.6%), exposing this population to socio-environmental contexts that increase disease risk [33].

Environmental racism appears to be an important determinant. This term explains conditions in which traditionally marginalized communities are disproportionately affected by environmental issues. It was initially used in the context of protest against the disposal of toxic waste in predominantly Black communities in the United States [34]. In Brazil, these disparities become evident through sanitation issues. For example, 75% of Black and 68.9% of mixed-race populations had access to sewage networks, compared to 83.5% of White populations [35–37].

Environmental factors contribute directly or indirectly to vector proliferation and the maintenance of ZIKV transmission [16]. In Rio Grande do Norte, where the highest relative-risk cluster and the only increasing incidence trend were identified, socio-environmental conditions may have favored sustained transmission. Limited access to regular water supply can promote water storage practices that create breeding sites for Aedes aegypti, while deficiencies in waste collection may increase the availability of containers capable of retaining standing water [36].

Supporting this hypothesis, areas of Natal with higher arbovirus incidence have also been associated with greater accumulation of solid waste [38]. Together with the documented presence of Aedes aegypti in urban and non-urban environments, these factors may contribute to the persistence of ZIKV transmission in the state.

Brazil experienced a reduction in incidence rates throughout the analyzed period, as reported in this study and previous investigations [6, 39, 40]. In 2015, an increase in microcephaly cases was observed in the Northeast with no apparent cause. Early that year, surveillance services also reported exanthematous disease outbreaks in the region, later attributed to ZIKV after laboratory confirmation. Upon observing the association between both phenomena, the hypothesis was raised that the neurological syndrome was caused by the virus. Several studies were conducted, driven by coordination among institutions, to investigate this clinical-epidemiological phenomenon [41].

In the initial years, particularly from 2015 to 2017, reductions in incidence rates were noted not only for Zika but also for other arboviroses [30, 42, 43]. Measures taken to combat Aedes aegypti were associated with the decline in case numbers [44]. For instance, a campaign conducted and publicized in November 2015 encouraged the elimination of mosquito breeding sites through household cleaning actions [45]. A budgetary measure in March 2016 allowed the release of BRL 420 million for actions to combat microcephaly and Aedes aegypti [45]. These actions likely impacted the reduction of cases in subsequent years, not only for Zika but also for other vector-borne diseases (dengue and chikungunya). The strengthening of vector-control activities following the public health emergency may have further contributed to reducing mosquito populations and interrupting transmission chains.

Moreover, factors related to arbovirus evolution may be associated with the decreasing trend after the initial Zika outbreak. Based on the context of other arbovirus outbreaks (dengue and chikungunya), it has been observed that as the first viral strain spreads, the number of susceptible individuals decreases, reducing the likelihood that a more adapted mutant variant establishes and leads to a prolonged outbreak [46, 47]. Similarly, the large-scale circulation of ZIKV during the epidemic period may have increased population immunity, thereby reducing the pool of susceptible individuals and contributing to the decline in incidence observed in subsequent years. Additionally, the possibility of underreporting, particularly among asymptomatic and mild cases, should be considered when interpreting temporal reductions in reported incidence.

The COVID-19 pandemic may also have contributed to the reduction in reported Zika cases, as observed for other arboviruses and infectious diseases in Brazil. In 2020, dengue notifications decreased by 38.8% compared with the previous year [48], a pattern that was also reported for leishmaniasis [49], schistosomiasis [50], tuberculosis [51], and HIV infection [52]. One possible explanation is the reallocation of healthcare resources and surveillance efforts toward the COVID-19 response, which may have reduced the detection and reporting of other diseases. Additionally, social distancing measures and prolonged time spent at home may have encouraged greater attention to household environments and the elimination of potential Aedes aegypti breeding sites.

Several explanations exist for this phenomenon: (i) reduced healthcare seeking due to fear of coronavirus infection [53]; (ii) limited operating hours of health services [53]; (iii) iii. redirection of epidemiological surveillance efforts toward COVID-19 case identification [40]; and (iv) reduced number of essential diagnostic procedures due to social isolation during the pandemic [54].

Throughout the analyzed years, Rio Grande do Norte was the only state to demonstrate an increasing trend in Zika incidence and was also identified as part of the highest relative-risk cluster. This finding is consistent with data reported by the Brazilian Ministry of Health, which indicated that, in 2022, five of the seven municipalities with the highest numbers of Zika cases in the country were located in Rio Grande do Norte [55, 56].

This pattern may be associated with a combination of factors. The widespread presence of Aedes aegypti in both urban and non-urban environments has been documented in Rio Grande do Norte and may contribute to sustained viral transmission [57]. Additionally, differences in surveillance sensitivity, healthcare-seeking behavior, diagnostic capacity, and reporting practices may have increased case detection compared with other regions. Population mobility and the continued circulation of arboviruses in the Northeast region may also favor the maintenance of transmission chains.

During the study period, the Northeast and Central-West regions presented high-risk clusters that should be prioritized for public health interventions. Spatial and space-time analyses also identified high-risk clusters in these areas. These findings are consistent with another study, which reported, from 2015 to 2021, clusters with more municipalities in the Central-West (n = 284), Southeast (n = 117), and Northeast (n = 68) [6].

Also in 2016, the Central-West region began to show the highest incidence rates (222 per 100,000 inhabitants), although the first cases had been identified in the Northeast [58]. This transition, in which high-risk Zika clusters became more present in the Central-West region in 2016, was highlighted in a study that analyzed the geospatial association between Zika virus and microcephaly [39].

This transition phenomenon can be explained by different factors related to Aedes aegypti. Being a vector widely adapted to the Brazilian context, ZIKV had a high probability of spreading throughout the country. Urbanization and climate are important factors for this adaptation [59]. Urban mobility also contributes to the transmission chain, as it allows rapid disease spread through multiple simultaneous foci, in combination with vector dispersal capacity [60].

The Brazilian Central-West also has local factors that increase epidemic risk: (i) changes in climatic conditions, with abnormally high temperatures, are associated with increased dengue incidence rates and were detected in this region [59]; (ii) it showed the highest growth rate of urban concentrations (clustered municipalities) from 2010 to 2022 among the country’s regions [18]. Areas with a high degree of urbanization had higher incidence rates of this arbovirus [59]; and (iii) the Cerrado biome in this region has been replaced by agriculture, with a 25% reduction in its area from 1985 to 2021 [61]. Deforestation is cited as a factor favoring the vector’s reproductive cycle by reducing the capacity to regulate the local microclimate [62].

These findings have important implications for public health planning. In the persistent high-risk clusters identified in the Central-West and Northeast regions, interventions should prioritize the strengthening of entomological and epidemiological surveillance, allowing the early detection of increases in vector density and ZIKV transmission. Targeted vector-control measures, including the elimination of breeding sites, improved solid waste management, and actions to ensure regular water supply, may be particularly relevant in municipalities with recurring high incidence. Given the role of urbanization and population mobility in sustaining transmission, integrated actions involving urban planning, environmental management, and community engagement should also be encouraged. In addition, the prioritization of high-risk clusters identified in this study may support resource allocation and the implementation of timely prevention and response strategies.

Even considering methodological precautions, this study has limitations. Secondary data from SINAN, a passive surveillance system, were used, and reported cases likely underestimate the true burden of Zika virus infection due to asymptomatic and mild cases. We also acknowledge that reliance on clinical-epidemiological confirmation may introduce some degree of misclassification, particularly in settings where other arboviruses with similar clinical presentations co-circulate. Regional differences in healthcare access, diagnostic capacity, and surveillance practices may also affect notification completeness and influence the spatial patterns observed. In addition, climatic factors were not included in the analysis, and missing information on education and pregnancy limited the assessment of these variables. Finally, Joinpoint regression evaluates trends across the entire study period and does not allow specific years, such as those affected by the COVID-19 pandemic, to be analyzed in isolation.

Conclusion

This study identified a predominance of confirmed Zika cases among women, adults, and individuals classified as mixed-race in Brazil between 2016 and 2023. Although a declining trend in incidence was observed nationwide, marked regional heterogeneity persisted across federative units. High-risk transmission clusters were identified, particularly in the Northeast and Central-West regions.

These findings highlight priority areas for targeted public health interventions. Strengthening surveillance systems is essential, especially in regions with persistent transmission. Particular attention should be given to Rio Grande do Norte, the only state that exhibited a significant increasing trend in incidence and that was included in the highest relative-risk cluster. Control strategies should be tailored to local and regional contexts, taking into account environmental, socioeconomic, and health system factors that influence disease dynamics.

Acknowledgements

The authors thank the Oswaldo Cruz Foundation (Fiocruz), National Council for Scientific and Technological Development (CNPq)—Ministry of Science, Technology, and Innovation of Brazil and Department of Science and Technology of Secretariat of Science, Technology and Innovation and the Health Economic Industrial Complex of Ministry of Health of Brazil – MoH. C.D.F.d.S. has a scientific productivity scholarship grant from CNPq.

Author contributions

MCSS, PEO, PSvE, RPSN, HRMJ, VCP, SLPN, AGSJ, TSM, CAF MBS, CPT, DPGDA, DECA, RFC, CDFS: Conceived and designed the paper, collected the data, reviewed the data, wrote the paper, and approved the final version of the manuscript.MCSS, CDFS and RFC: Conceived and designed the paper, collected the data, reviewed the data, wrote the paper, and approved the final version of the manuscript. RFC, MCSS, JVJSJ and CDFS: Conceived and designed the paper, collected the data, reviewed the data, wrote the paper, and approved the final version of the manuscript.

Funding

National Council for Scientific and Technological Development (CNPq), Ministry of Science, Technology, and Innovation, and the Department of Science and Technology of the Secretariat of Science, Technology and Innovation and the Health Economic-Industrial Complex of the Ministry of Health (MoH), Brazil. C.D.F.S. and R.F.C. received research productivity fellowships from CNPq.

Data availability

The study used secondary data in the public domain, which meant that it did not need to be examined by the Research Ethics Committee. Link: [http://tabnet.datasus.gov.br/cgi/deftohtm.exe? sinannet/cnv/ltape.def](http:/tabnet.datasus.gov.br/cgi/deftohtm.exe? sinannet/cnv/ltape.def).

Declarations

Ethics approval and consent to participate

The study used only secondary data from public domain information systems where individuals cannot be identified. Therefore, research ethics committee approval was not required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The study used secondary data in the public domain, which meant that it did not need to be examined by the Research Ethics Committee. Link: [http://tabnet.datasus.gov.br/cgi/deftohtm.exe? sinannet/cnv/ltape.def](http:/tabnet.datasus.gov.br/cgi/deftohtm.exe? sinannet/cnv/ltape.def).

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