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Article

Analysis of the Spatiotemporal Spread of COVID-19 in Bahia, Brazil: A Cluster-Based Study, 2020–2022

by
Ramon da Costa Saavedra
1,*,†,
Rita Carvalho-Sauer
1,†,
Maria Yury Travassos Ichihara
2,
Maria da Conceição Nascimento Costa
3,
Enio Silva Soares
1 and
Maria Gloria Teixeira
3
1
Bahia State Health Department, Salvador 40280-000, Brazil
2
Oswaldo Cruz Foundation, Salvador 40296-710, Brazil
3
Institute of Collective Health, Federal University of Bahia, Salvador 40110-040, Brazil
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
COVID 2025, 5(7), 109; https://doi.org/10.3390/covid5070109
Submission received: 11 June 2025 / Revised: 6 July 2025 / Accepted: 11 July 2025 / Published: 13 July 2025
(This article belongs to the Special Issue Airborne Transmission of Diseases in Outdoors and Indoors)
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Abstract

Background: The COVID-19 pandemic progressed unevenly across the 417 municipalities of Bahia, Brazil. Pinpointing where and when risk peaked is vital for preparing for future emergencies. Methods: We performed an ecological, spatiotemporal study using COVID-19-confirmed cases in Bahia, Brazil, from January 2020 to December 2022. A discrete Poisson space–time scan in SaTScan-identified clusters. For each cluster, we calculated relative risk (RR) and Log Likelihood Ratio, considering p < 0.05 as significant. Results: A total of 33 clusters were detected; 25 statistically significant. The largest cluster (164 municipalities; May 2020–June 2021) comprised 702,720 observed versus 338,822 expected cases (RR = 2.8). Two overlapping large clusters (185 and 136 municipalities) during January–February 2022—coinciding with Omicron circulation—showed RR > 2.0. Localized clusters reached RR > 3.0. Spatially, risk concentrated in the south, southwest, and east of the state, with isolated countryside outbreaks. Conclusions: The heterogeneous spatiotemporal dynamics of COVID-19 in Bahia underscore the value of cluster detection for targeted surveillance and resource allocation. We recommend employing statistical techniques for early detection and control, as well as conducting further studies on socioeconomic and behavioral factors.

1. Introduction

The COVID-19 pandemic has emerged as the most impactful public health event in recent decades, exposing significant challenges in containing the spread of its etiological agent, SARS-CoV-2, and in managing healthcare systems [1]. From the first recorded cases in China in December 2019 to its rapid dissemination across continents, this viral disease has exhibited complex and heterogeneous transmission patterns determined by biological, social, economic, and environmental factors [2].
By the first half of 2020, Brazil quickly became one of the epicenters of this health emergency, consistently ranking among the countries with the highest number of accumulated cases and deaths [3]. Due to its continental dimensions and deep socioeconomic inequalities, the course of the pandemic in Brazil varied significantly across regions, states, and municipalities [4]. This scenario was exacerbated by the absence of a nationally coordinated strategy for containment and mitigation [5,6].
In Bahia, COVID-19 presented unique epidemiological characteristics due to the heterogeneity of its territory, which includes densely populated urban zones and remote rural areas. During the first two years of the pandemic, epidemic peaks of varying intensity occurred, with the highest incidence recorded in the early weeks of 2022. Compared to other Brazilian states, Bahia maintained relatively lower incidence and mortality rates [7,8]. However, it is important to highlight that territorial heterogeneity and disparities in the provision of health and testing services may have impacted the quality of epidemiological surveillance in different regions of the state, with potential implications for the reporting of cases and deaths, especially in the initial stages of the pandemic.
The emergence and dissemination of SARS-CoV-2 variants posed new challenges in pandemic control due to their distinct transmissibility and severity characteristics, resulting in different incidence and mortality patterns [9]. Brazil played a central role in this scenario, as evidenced by the identification of the Gamma variant (P.1) in Manaus, which rapidly spread nationally and internationally. The high viral load in the country, exacerbated by regional inequalities and delays in mass vaccination efforts, intensified the circulation of variants of concern [10]. In Bahia, different subtypes shaped the behavior of epidemic waves, with peaks frequently associated with the predominance of highly transmissible variants [11].
Although the pandemic has been overcome, it remains essential to investigate the transmission dynamics of COVID-19. Retrospective analyses of spatiotemporal patterns and the factors that influenced SARS-CoV-2 dissemination can provide relevant information for countries to prevent and prepare for future public health emergencies, particularly those involving emerging respiratory viruses [12]. More specifically, comparing cluster detection methods across regions can enhance surveillance protocols and help detect outbreaks earlier, when interventions are more feasible [13]. Furthermore, mapping excess-risk hotspots supports fairer allocation of scarce resources and underpins the locally adaptive preparedness metrics promoted in recent calls [14].
This study aims to detect and quantify space–time clusters of COVID-19 incidence and describe their magnitude, duration, and geographic extent in the state of Bahia, Brazil, from January 2020 to December 2022. We also aim to discuss possible explanations for the identified clusters and provide recommendations to strengthen state epidemiological surveillance.

2. Materials and Methods

2.1. Study Design and Period

This is an ecological, spatiotemporal study of confirmed COVID-19 cases in the state of Bahia, Brazil, conducted with the municipality of residence as the spatial unit of analysis and the month of occurrence as the temporal unit, covering the period from 2020 to 2022.

2.2. Study Area and Population

Bahia is in the Northeast region of Brazil and comprises 417 municipalities, which are organized into 28 Administrative Regions and 9 health macro-regions. It is the fourth most populous state in Brazil and the fifth largest in territorial extension, covering 6.6% of the country’s geographic area and 36.3% of the region [15,16].
The study population consists of 14,141,626 residents of Bahia, who were analyzed concerning the occurrence of COVID-19. The investigation considered confirmed cases of the disease according to the criteria established by the Brazilian Ministry of Health, covering the years 2020 to 2022 [15,16].

2.3. Data Sources

Data on COVID-19 cases, categorized by municipality of residence and month of occurrence, were obtained from the Bahia State Health Department’s website, through the Flu Notification System (e-SUS Notifica) [17], and the Acute Respiratory Distress Syndrome Epidemiological Surveillance System (Sivep-Gripe) [18]. The e-SUS Notifica database captures all mild-to-moderate suspected COVID-19 illnesses reported by primary-care facilities since 2020, while SIVEP-Gripe recorded all hospitalized severe acute respiratory infections.
COVID-19 case confirmation can be achieved through various laboratory diagnostic methods, such as molecular biology tests (RT-qPCR and RT-LAMP), serological tests (ELISA, CLIA, and ECLIA), and rapid antigen tests. Additionally, clinical imaging techniques, like high-resolution computed tomography, can identify peripheral, bilateral, or multifocal ground-glass opacities, with or without visible intralobular lines in patients with influenza-like symptoms [15,16].
Population data and geographic coordinates (latitude and longitude) necessary for calculating the incidence rate, as well as the centroid of each municipality, were obtained from the Brazilian Institute of Geography and Statistics (IBGE) [15].
The final analytical file comprised 1,746,512 confirmed cases, geocoded to all 417 municipalities of Bahia, and linked to annual population estimates to calculate incidence rates.

2.4. Analytical Procedures

To analyze the spatiotemporal distribution of COVID-19 and identify high-risk clusters for disease incidence in Bahia from January 2020 to December 2022, data were aggregated by month of occurrence and municipality of residence. Spatiotemporal cluster analysis was conducted using the discrete Poisson statistical model, which is appropriate for count data with heterogeneous populations. This model assumes that the number of cases in each spatial unit follows a Poisson distribution proportional to the local population [19], which, under the null hypothesis, the risk is uniform within and outside each scanning window. It is important to note that this assumption may be violated in the presence of unmeasured spatial confounders or over-dispersion, potentially inflating type I errors.
For each municipality in Bahia (n = 417), geographic coordinates (latitude and longitude), monthly average population, and the number of COVID-19 cases were provided. The implemented spatiotemporal scan procedure creates virtual cylinders over the map, where the circular base represents the geographic area and the height corresponds to the time interval. The base radius and temporal duration of the cylinders vary to identify potential clusters with high incidence rates [20].
The analysis considered clusters of different spatial sizes and temporal intervals, with a maximum search radius of 50% of the population at risk and a maximum temporal window of six months. The statistical criterion adopted was the Log Likelihood Ratio (LLR), and the statistical significance of the clusters was assessed using Monte Carlo tests with random simulations, with 999 permutations, for all spatial analyses. The identified clusters were classified in terms of relative risk (RR), reflecting the ratio between observed and expected cases within the analyzed area and period. Only statistically significant clusters (p < 0.05) were considered for result interpretation. Spatiotemporal analysis was performed using SaTScan software version 10.2.1. Georeferencing of COVID-19 cases used the geobr package, and the graphs were generated using the ggplot2 package in R software version 4.3.1.

3. Results

The spatiotemporal analysis identified a total of 33 COVID-19 clusters in Bahia between January 2020 and December 2022, of which 25 clusters were statistically significant (p < 0.05; Table 1). The most prominent cluster (Cluster 1) encompassed 164 municipalities (Appendix A, Table A1) within a geographic range of 586.3 km and was observed from 1 May 2020, to 30 June 2021. This extensive cluster, with a risk population of approximately 265.6 million (aggregated across the entire analysis window), recorded 702,720 cases compared to the 338,822 expected cases, yielding an observed-to-expected ratio of 2.07, an adjusted relative risk (RRa) of 2.8, and a Log Likelihood Ratio (LLR) of 200,435.8 (p < 0.0001) (Table 1).
The municipalities comprising Cluster 1 span 20 regions and 6 macro-regions of the state, with more than half (61.7%) located in the South and Southwest macro-regions of Bahia. Municipalities from the East (22.8%), where the state capital and metropolitan region are situated, and the Extreme-South (11.4%) also stand out. In this cluster, the highest accumulated incidence rates per 100,000 inhabitants were recorded in the municipalities of Maracás (19,109.5), Itabuna (17,080.7), Ibirataia (16,254.5), Conceição do Almeida (18,543.6), and Itororó (16,024.1), with the first three located in the South macro-region. In the same period, the accumulated incidence of COVID-19 in the state of Bahia was 7563.97/100,000 inhabitants (Table 1).
Clusters identified during the early months of 2022 also revealed high risks. Cluster 2, detected from 1 January to 28 February of that year, involved 185 municipalities (Appendix A, Table A1) with a risk population of 265.9 million, presenting 102,534 observed cases versus 46,983 expected cases, resulting in an observed/expected ratio of 2.18, an RRa of 2.26, and an LLR of 25,385.5 (p < 0.0001). This cluster involved municipalities from 18 regions and 5 health macro-regions, with the majority (70.8%) located in the South and Southwest macro-regions. Municipalities with the highest accumulated incidence rates per 100,000 inhabitants in this cluster were as follows: Maetinga (7879.3), Aratuípe (6340.4), Ibiassucê (5356.5), Ibirapuã (4645.3), and Paramirim (3692.0), three of them belonging to the Southwest macro-region. The accumulated incidence of COVID-19 in Bahia during this same period was 1281.25/100,000 inhabitants (Table 1).
Cluster 3, also spanning from 1 January to 28 February 2022, involved 136 municipalities (Appendix A, Table A1) from 10 regions and 5 health macro-regions, with a predominance of the Central-East and Northeast macro-regions, which together accounted for 58.1% of the total municipalities. This cluster had a risk population of 183.1 million, with 66,092 observed cases compared to 32,340 expected cases (observed/expected ratio of 2.04, RRa of 2.08, LLR = 13,821.2, p < 0.0001) (Table 1).
Other statistically significant clusters were more spatially localized and temporally brief. For example, Cluster 5, which comprised a single municipality (Appendix A, Table A1) during the period from 1 September to 31 October 2022, demonstrated the highest relative risk among isolated clusters. This cluster, with a risk population of 1,933,320, reported 1139 cases compared to 353.1 expected cases, yielding an observed-to-expected ratio of 3.23, an annual incidence of 352.8 cases per 100,000 inhabitants, and an LLR of 548.1 (p < 0.0001). Additional statistically significant clusters (Clusters 6 to 25) exhibited relative risks ranging from 1.42 to 3.37 and LLR values between 61.9 and 1949.1, highlighting epidemic peaks in small spatial aggregates during distinct periods. Conversely, Clusters 26 to 33 did not reach statistical significance (p-values ranging from 0.092 to 0.99) and were not further considered in the risk characterization (Table 1).
Figure 1 illustrates high-risk COVID-19 clusters in Bahia, Brazil, from 2020 to 2022, categorized by magnitude of relative risk, highlighting the centroids of 33 clusters identified during different phases of the pandemic.
Figure 2 overlays these clusters on a satellite map, providing geographic context for the affected areas. The distribution reveals concentrations of clusters in the North, Northeast, and South of the state, as well as additional clusters in coastal and inland areas.

4. Discussion

The spatiotemporal analysis of the COVID-19 spread in Bahia between 2020 and 2022 revealed significant variations in the transmission of SARS-CoV-2, presenting patterns that probably reflect the interaction between epidemiological, sociodemographic, sociopolitical, and healthcare infrastructure factors. The results of this study highlight the presence of 25 clusters that demonstrated important aspects in terms of geographic extent, duration, and relative risk. These findings support the hypothesis that the pandemic did not behave uniformly across the state of Bahia, as it presented localized outbreaks or epidemic peaks, distinct both spatially and temporally, associated with different epidemic waves and the emergence of viral variants.
Cluster 1, the largest and longest lasting, encompassed 164 municipalities and remained active for fourteen months, from May 2020 to June 2021. This period corresponds to the initial phase of the pandemic, marked by the introduction and rapid spread of the virus, ineffective and inconsistent responses across federal, state, and municipal governments [21,22], and the slow implementation of the vaccination campaign during the first half of 2021 [23]. The elevated relative risk (RR = 2.8) and high observed/expected ratio (2.07) reflect the rapid and prolonged spread of the virus, possibly driven by high population density and intercity mobility. In this context, the health macro-regions of Southern and Southwestern Bahia were important centers of viral transmission.
In 2020, following the occurrence of the first imported cases of COVID-19 due to air travel, the expansion of the disease in Bahia began to be influenced by population movements associated with cultural and political events throughout the year [24]. Although official June festivals, traditionally characterized by intense internal migrations, were suspended by state decree, the excessive flow of people during this period contributed to the virus’s spread to smaller municipalities [25]. In the second half of the year, municipal elections also mobilized large gatherings, from electoral campaigns to voting day, favoring new transmission waves. By the end of the year, Christmas and New Year celebrations intensified movements, family gatherings, and clandestine parties, resulting in a significant increase in cases in the first weeks of 2021. These events highlight the impact of social dynamics and mobility on the spread of COVID-19 in the state [26,27,28].
The distribution of cases in certain regions of the state may also be related to key municipalities, with higher economic activity, especially in agribusiness, which determines intense circulation of people and vehicles in surrounding areas. The existence of important federal highways connecting municipalities in the interior of Bahia to the Southeast region of the country, where COVID-19 had high incidence, is also noteworthy. These routes likely facilitated the spread of SARS-CoV-2 in these areas [29,30].
Larger urban centers were epicenters of the COVID-19 pandemic in Bahia, acting as vectors for the virus’s spread to smaller municipalities. This dynamic is associated with intrinsic characteristics of these metropolitan areas, such as high population density, which plays a key role in disease transmission, especially during the community transmission phase. Evidence of this relationship has been documented in studies conducted in various geographical contexts, such as China, the United States, India, and countries in Africa, all of which emphasize the influence of human crowding on accelerating viral transmission [31,32,33,34,35].
Although this study did not directly analyze variables related to socioeconomic factors, it must be considered that SARS-CoV-2 transmission tends to be higher in areas of greater social vulnerability. In this regard, many municipalities in the interior that formed part of the epidemiologically relevant clusters in this study have high social vulnerability index (SVI) and low human development index (HDI). Even in large urban centers, there are areas marked by poverty, which increases the risk of virus spread [36,37,38].
The association between the occurrence of certain clusters and the circulation of variants of concern is also relevant. The Gamma variant (P.1), predominant in Brazil in 2021 [39], was possibly associated with the intensification of transmission during the formation of Cluster 1, contributing to its greater duration and scope. Clusters 2 and 3, in early 2022, coincided with the circulation of the Omicron variant (BA.1), characterized by high transmissibility and partial immune escape [40]. Even with the advance of vaccination, these factors favored rapid and intense outbreaks, as evidenced by the high relative risks (RR = 2.26 and 2.08). Cluster 5, in turn, was brief, but presented the highest relative risk in the study (RR = 3.37), which suggests the influence of specific contextual factors, such as social gatherings, inequalities in healthcare access, or the introduction of the virus into previously less-affected areas, underscoring the need for differentiated approaches to pandemic control, with consideration of the particularities of each region [41].
The high observed/expected ratios and significant relative risks indicate that, even with advancements in vaccination, the circulation of new variants continued to generate localized outbreaks. These findings emphasize the importance of genomic surveillance and the adaptation of control strategies in response to viral evolution, particularly after the initiation of the COVID-19 vaccination campaign [42].
The geographic distribution of the clusters, with concentrations in the East, South, and Southwest of the state, as well as in coastal and inland areas, reflects the heterogeneity of the Bahian territory. Densely populated urban areas, such as Salvador, Feira de Santana, Ilhéus, and Itabuna, also acted as epicenters of dissemination, while rural and hard-to-reach regions displayed distinct patterns, possibly related to lower mobility and greater underreporting. The overlap of the clusters on the satellite map provides information into the relationship between transmission dynamics and geographic features, such as proximity to highways and population density [29,30].
It is also important to recognize that the nature and timing of anti-epidemic measures may have influenced the formation and progression of the identified clusters. Variations in the implementation of public health interventions, such as lockdowns, travel restrictions, mandatory mask use, testing strategies, and vaccine rollout, likely contributed to differences in transmission dynamics across municipalities. Previous studies have highlighted how sociopolitical responses to the pandemic shaped its spread across regions and countries. While such variables were not included in our current analysis due to data limitations, future research should aim to integrate these dimensions to better understand the multifactorial drivers behind epidemic clustering and to inform more responsive and context-specific surveillance and control strategies. The persistence of these clusters in new epidemic waves and/or future epidemics may be related to the general context, including the anti-epidemic policies adopted, population adherence to preventive measures, and the Social Vulnerability Index of the Municipalities [43,44].
The methodology used, based on the Poisson model and spatiotemporal scanning analysis, proved robust for identifying patterns of transmission and risk areas. However, it is important to recognize the inherent limitations of secondary data analysis, which may be subject to underreporting and delays in data inclusion, as well as variability in data quality depending on the healthcare infrastructure of each region. Additionally, the analysis did not explicitly consider socioeconomic and behavioral factors that may have influenced transmission dynamics. Also, cluster detection with the discrete Poisson model assumes that every individual in the study area is exposed to the same baseline risk, so the expected number of cases in each geographic unit is simply proportional to its population size, and when the true risk varies systematically across units with markedly different demographic compositions, this equal-risk assumption can be violated, potentially producing biased cluster estimates [45].
Additionally, it is important to consider whether the spatial–temporal patterns identified in this study have the potential to be repeated in the context of future health emergencies. The configuration of the observed clusters largely reflects the specificities of the COVID-19 pandemic, such as the dynamics of virus introduction, the characteristics of the circulating variants, population mobility, and institutional response over time. However, many of the structural factors that contributed to the formation of these clusters remain unchanged, which suggests that part of the observed spatial distribution may be repeated in future epidemics with a similar profile. Thus, although the clusters are characteristic of the pandemic period analyzed, they also signal areas with persistent vulnerabilities, which should be prioritized in the preparation and response to new epidemic events.
Our study offers concrete implications for public policy. In line with the post-2022 international momentum for stronger epidemic preparedness, crystallized in the WHO Pandemic Accord of 2025 [46] and related initiatives, we recommend the implementation of municipal-level, early-warning dashboards that display SaTScan-detected clusters in near real time; the prioritization of mobile vaccination and rapid-testing units for high-risk municipalities; the early allocation of medical and healthcare resources guided by cluster analyses; and contingency plans built around locally adaptable, metrics-based triggers that can be updated as new evidence emerges.

5. Conclusions

This statewide, three-year spatiotemporal reconstruction demonstrates that COVID-19 risk in Bahia was neither random nor homogeneous. We identified the areas most vulnerable to SARS-CoV-2 transmission, providing a foundation for further investigation and for targeted interventions that address the drivers of these disparities. In addition, we offer evidence-based recommendations for public policy.
For the scientific community, our dataset, covering all 417 municipalities, serves as a benchmark for evaluating cluster detection algorithms that can be adapted to the ongoing management of COVID-19 and other pathogens of interest, such as influenza and arboviruses.
Compared to previous studies in Bahia, which examined shorter time frames, our analysis spans both the pre- and post-vaccine periods and quantifies the magnitude and duration of clusters, thereby highlighting the areas that should be prioritized as the state prepares for future health emergencies.
Looking ahead, we urge health authorities to embed real-time spatiotemporal cluster monitoring into routine surveillance dashboards, pair these alerts with pre-defined response protocols, and regularly drill scenarios using the risk maps presented here. We also encourage cross-sector partnerships, linking epidemiologists, social scientists, and logistics planners, to adapt this framework to other pathogens. Transforming retrospective analysis into an operational early-warning system will shift public health from reactive crisis management to proactive, evidence-based preparedness, setting a benchmark for similarly diverse regions worldwide.

Author Contributions

Conceptualization, R.d.C.S., M.Y.T.I. and M.G.T.; methodology R.C.-S.; software, R.C.-S.; validation, R.d.C.S., M.d.C.N.C., M.Y.T.I. and M.G.T.; formal analysis, R.d.C.S., E.S.S. and R.C.-S.; investigation, R.d.C.S. and R.C.-S.; resources, R.d.C.S.; data curation, R.d.C.S. and E.S.S.; writing—original draft preparation, R.d.C.S. and R.C.-S.; writing—review and editing, R.d.C.S., R.C.-S., M.d.C.N.C., M.Y.T.I., E.S.S. and M.G.T.; supervision, M.d.C.N.C., M.Y.T.I. and M.G.T.; project administration, M.G.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study utilized publicly available, non-identifiable data, which exempts it from the requirement of submission to an ethics committee, in accordance with the resolution of the National Health Council (Resolution No. 510, of 7 April 2016).

Informed Consent Statement

Patient consent was waived due to the use of anonymized data with unrestricted public access.

Data Availability Statement

The COVID-19 data used for this research are available for download on the website of the Bahia State Health Department, at https://dados.ba.gov.br/dataset/38f0f19d-fd41-4213-8bd5-c62baffb1ec9/resource/62eee774-8dab-49c9-ae54-c250c6eab25d/download/onedrive_1_27-06-2023.zip (visit date: 25 June 2025) and population at <http://tabnet.datasus.gov.br/cgi/deftohtm.exe?ibge/cnv/popsvs2024br.def> (visit date: 25 June 2025). The geographical data (latitude and longitude) were obtained using the geobr package <https://ipeagit.github.io/geobr/> with the R programming language, but can also be found on the website <astro.if.ufrgs.br/br.htm> (visit date: 25 June 2025).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SVISocial Vulnerability Index
HDIHuman Development Index
e-SUS NotificaFlu Notification System
SivepAcute Respiratory Distress Syndrome Epidemiological Surveillance System
IBGEBrazilian Institute of Geography and Statistics
LLRLog Likelihood Ratio
RRRelative Risk

Appendix A

Table A1. Municipalities comprising each spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022.
Table A1. Municipalities comprising each spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022.
Cluster IDMunicipalities Included
01Una, São José da Vitória, Santa Luzia, Buerarema, Arataca, Jussari, Itabuna, Camacan, Itapé, Canavieiras, Ilhéus, Mascote, Barro Preto, Ibicaraí, Itaju do Colônia, Pau Brasil, Itajuípe, Floresta Azul, Uruçuca, Almadina, Belmonte, Santa Cruz da Vitória, Coaraci, Potiraguá, Firmino Alves, Itororó, Itapebi, Itacaré, Itapitanga, Itapetinga, Ibicuí, Aurelino Leal, Ubaitaba, Itarantim, Santa Cruz Cabrália, Gongogi, Itagimirim, Nova Canaã, Iguaí, Maraú, Dário Meira, Eunápolis, Itagibá, Maiquinique, Ubatã, Ibirapitanga, Caatiba, Barra do Rocha, Camamu, Macarani, Itambé, Ipiaú, Ibirataia, Aiquara, Igrapiúna, Boa Nova, Itagi, Planalto, Poções, Porto Seguro, Barra do Choça, Piraí do Norte, Jitaúna, Nova Ibiá, Ribeirão do Largo, Gandu, Ituberá, Itabela, Apuarema, Guaratinga, Itamari, Nilo Peçanha, Bom Jesus da Serra, Taperoá, Jequié, Wenceslau Guimarães, Teolândia, Vitória da Conquista, Cairu, Manoel Vitorino, Presidente Tancredo Neves, Encruzilhada, Anagé, Jaguaquara, Jucuruçu, Valença, Mirante, Itamaraju, Cravolândia, Itaquara, Prado, Lafaiete Coutinho, Mutuípe, Jiquiriçá, Caetanos, Itiruçu, Belo Campo, Ubaíra, Cândido Sales, Santa Inês, Laje, Lajedo do Tabocal, Jaguaripe, Vereda, Aratuípe, Caraíbas, Irajuba, São Miguel das Matas, Maracás, Santo Antônio de Jesus, Muniz Ferreira, Amargosa, Itanhém, Tremedal, Brejões, Tanhaçu, Vera Cruz, Alcobaça, Nazaré, Varzedo, Teixeira de Freitas, Dom Macedo Costa, Aracatu, Elísio Medrado, Planaltino, Contendas do Sincorá, Nova Itarana, Maetinga, Conceição do Almeida, Salinas da Margarida, São Felipe, Itaparica, Milagres, Medeiros Neto, Maragogipe, Salvador, Iramaia, Barra da Estiva, Saubara, Piripá, Lauro de Freitas, Caravelas, Sapeaçu, Castro Alves, Santa Teresinha, Madre de Deus, Cruz das Almas, Presidente Jânio Quadros, São Félix, Marcionílio Souza, Simões Filho, Itatim, Ituaçu, Iaçu, Cachoeira, Lajedão, Muritiba, São Francisco do Conde, Candeias, Governador Mangabeira, Cabaceiras do Paraguaçu, Ibirapuã, Cordeiros, Brumado.
02Caravelas, Nova Viçosa, Teixeira de Freitas, Ibirapuã, Alcobaça, Mucuri, Vereda, Lajedão, Prado, Medeiros Neto, Itamaraju, Itanhém, Jucuruçu, Itabela, Porto Seguro, Guaratinga, Eunápolis, Santa Cruz Cabrália, Itagimirim, Itapebi, Belmonte, Potiraguá, Itarantim, Mascote, Maiquinique, Canavieiras, Pau Brasil, Camacan, Macarani, Santa Luzia, Itapetinga, Ribeirão do Largo, Encruzilhada, Arataca, Una, Itaju do Colônia, Jussari, São José da Vitória, Itambé, Itororó, Buerarema, Itapé, Firmino Alves, Caatiba, Cândido Sales, Santa Cruz da Vitória, Ibicaraí, Floresta Azul, Itabuna, Nova Canaã, Barra do Choça, Vitória da Conquista, Barro Preto, Ilhéus, Almadina, Itajuípe, Ibicuí, Coaraci, Belo Campo, Planalto, Iguaí, Tremedal, Uruçuca, Itapitanga, Poções, Dário Meira, Piripá, Anagé, Aurelino Leal, Itacaré, Boa Nova, Cordeiros, Bom Jesus da Serra, Gongogi, Caraíbas, Ubaitaba, Itagibá, Maetinga, Itagi, Maraú, Presidente Jânio Quadros, Caetanos, Condeúba, Barra do Rocha, Ubatã, Aiquara, Ipiaú, Ibirapitanga, Mirante, Aracatu, Camamu, Ibirataia, Mortugaba, Manoel Vitorino, Jitaúna, Tanhaçu, Guajeru, Jequié, Igrapiúna, Jacaraci, Nova Ibiá, Apuarema, Piraí do Norte, Gandu, Itamari, Malhada de Pedras, Ituberá, Brumado, Nilo Peçanha, Wenceslau Guimarães, Caculé, Contendas do Sincorá, Lafaiete Coutinho, Rio do Antônio, Licínio de Almeida, Urandi, Teolândia, Taperoá, Jaguaquara, Ituaçu, Itiruçu, Ibiassucê, Cairu, Itaquara, Presidente Tancredo Neves, Barra da Estiva, Cravolândia, Lajedo do Tabocal, Maracás, Dom Basílio, Pindaí, Jiquiriçá, Valença, Mutuípe, Iramaia, Lagoa Real, Santa Inês, Ubaíra, Candiba, Livramento de Nossa Senhora, Irajuba, Laje, Planaltino, Sebastião Laranjeiras, Rio de Contas, Jussiape, Ibicoara, Caetité, Guanambi, Brejões, Jaguaripe, São Miguel das Matas, Aratuípe, Marcionílio Souza, Amargosa, Nova Itarana, Santo Antônio de Jesus, Muniz Ferreira, Varzedo, Elísio Medrado, Vera Cruz, Itaetê, Nazaré, Milagres, Dom Macedo Costa, Igaporã, Abaíra, Paramirim, Conceição do Almeida, Érico Cardoso, São Felipe, Salinas da Margarida, Iaçu, Palmas de Monte Alto, Itaparica, Iuiu, Maragogipe, Mucugê, Salvador, Santa Teresinha, Matina, Tanque Novo, Castro Alves, Itatim, Sapeaçu.
03Santa Brígida, Paulo Afonso, Pedro Alexandre, Jeremoabo, Coronel João Sá, Sítio do Quinto, Glória, Antas, Novo Triunfo, Rodelas, Cícero Dantas, Adustina, Fátima, Canudos, Macururé, Paripiranga, Banzaê, Heliópolis, Euclides da Cunha, Ribeira do Pombal, Chorrochó, Ribeira do Amparo, Uauá, Quijingue, Cipó, Monte Santo, Tucano, Itapicuru, Abaré, Curaçá, Nova Soure, Cansanção, Olindina, Nordestina, Araci, Crisópolis, Andorinha, Rio Real, Sátiro Dias, Teofilândia, Jaguarari, Acajutiba, Itiúba, Jandaíra, Biritinga, Santaluz, Aporá, Barrocas, Queimadas, Valente, Senhor do Bonfim, Inhambupe, Conceição do Coité, Serrinha, Juazeiro, Conde, Água Fria, Retirolândia, Lamarão, Filadélfia, Esplanada, São Domingos, Ichu, Ponto Novo, Cardeal da Silva, Ouriçangas, Entre Rios, Aramar, Santanópolis, Santa Bárbara, Antônio Gonçalves, Candeal, Gavião, Pindobaçu, Nova Fátima, Irará, Alagoinhas, Capim Grosso, Tanquinho, Riachão do Jacuípe, Caldeirão Grande, São José do Jacuípe, Araçás, Pedrão, Saúde, Caém, Capela do Alto Alegre, Quixabeira, Coração de Maria, Pé de Serra, Teodoro Sampaio, Itanagra, Campo Formoso, Feira de Santana, Catu, Pojuca, Serra Preta, Várzea da Roça, Anguera, Conceição do Jacuípe, Serrolândia, Terra Nova, Sobradinho, Mirangaba, Pintadas, Amélia Rodrigues, Mata de São João, Jacobina, São Gonçalo dos Campos, Várzea do Poço, São Sebastião do Passé, Ipecaetá, Antônio Cardoso, Mairi, Dias d’Ávila, Santo Amaro, Conceição da Feira, Camaçari, Santo Estêvão, Ipirá, São Francisco do Conde, Miguel Calmon, Candeias, Baixa Grande, Governador Mangabeira, Cachoeira, Cabaceiras do Paraguaçu, Muritiba, Simões Filho, Madre de Deus, São Félix, Rafael Jambeiro, Cruz das Almas, Saubara, Piritiba, Lauro de Freitas.
04Pilão Arcado, Campo Alegre de Lourdes, Buritirama, Remanso, Xique-Xique, Barra, Itaguaçu da Bahia, Sento Sé, Mansidão, Gentio do Ouro, Central, Jussara, Uibaí, Presidente Dutra, Santa Rita de Cássia, Morpará, São Gabriel, Irecê, Ibipeba, Casa Nova, Umburanas, Ipupiara, João Dourado, Wanderley, Ibititá, Lapão, Ibotirama, Cotegipe, América Dourada, Ourolândia, Brotas de Macaúbas, Barra do Mendes, Sobradinho, Barro Alto, Canarana, Muquém de São Francisco, Oliveira dos Brejinhos, Várzea Nova, Cafarnaum, Campo Formoso, Souto Soares, Morro do Chapéu, Cristópolis, Mirangaba, Angical, Mulungu do Morro, Brejolândia, Riachão das Neves, Bonito, Tabocas do Brejo Velho, Paratinga, Ibitiara, Seabra, Iraquara, Jacobina, Catolândia, Boquira, Antônio Gonçalves, Formosa do Rio Preto, Pindobaçu, Miguel Calmon, Juazeiro, Saúde, Utinga, Sítio do Mato, Serra Dourada, Wagner, Ibipitanga, Tapiramutá, Baianópolis, Palmeiras, Lençóis, Piritiba, Senhor do Bonfim, Novo Horizonte, Caém, Jaguarari, Filadélfia, Caldeirão Grande, Boninal, Ponto Novo, Barreiras, Macaúbas, Serrolândia, Várzea do Poço, Santana, Mundo Novo, Lajedinho, Andorinha, Quixabeira, Rio do Pires, Canápolis, Piatã, Bom Jesus da Lapa, Itiúba, Andaraí, Ruy Barbosa, Caturama, Capim Grosso, Mairi, Botuporã, Várzea da Roça, Ibiquera, Nova Redenção, Santa Maria da Vitória, Mucugê, São José do Jacuípe, Queimadas, Curaçá, Baixa Grande, Macajuba, Serra do Ramalho, Érico Cardoso, Abaíra, Tanque Novo, Luís Eduardo Magalhães, Uauá, Paramirim, Monte Santo, Gavião, Riacho de Santana, Capela do Alto Alegre, São Desidério, Cansanção, São Félix do Coribe, Boa Vista do Tupim, Pintadas, Itaetê, Nordestina, Itaberaba, São Domingos, Santaluz, Nova Fátima, Ibicoara, Jussiape, Rio de Contas, Matina, Valente, Igaporã, Pé de Serra, Abaré, Retirolândia, Marcionílio Souza, Chorrochó, Ipirá, Livramento de Nossa Senhora, Caetité, Coribe, Riachão do Jacuípe, Canudos, Iaçu, Quijingue, Iramaia, Carinhanha, Dom Basílio, Conceição do Coité, Correntina, Lagoa Real, Barra da Estiva, Euclides da Cunha, Araci, Palmas de Monte Alto, Ituaçu, Guanambi, Planaltino, Serra Preta, Macururé, Barrocas, Malhada, Ichu, Rafael Jambeiro, Feira da Mata, Itatim, Candeal, Nova Itarana, Maracás, Contendas do Sincorá, Teofilândia, Milagres, Tucano, Ibiassucê, Ipecaetá, Serrinha, Brumado, Tanquinho, Irajuba, Candiba, Santa Teresinha, Rio do Antônio, Anguera, Lajedo do Tabocal, Banzaê, Brejões, Rodelas, Iuiu, Pindaí, Santa Bárbara, Lamarão, Santo Estêvão, Malhada de Pedras, Itiruçu, Biritinga, Tanhaçu, Jeremoabo, Caculé, Antônio Cardoso, Feira de Santana, Ribeira do Pombal, Lafaiete Coutinho, Sebastião Laranjeiras, Amargosa, Elísio Medrado, Castro Alves, Santa Inês, Novo Triunfo, Cícero Dantas, Santanópolis, Cabaceiras do Paraguaçu, Licínio de Almeida, Jaborandi, Cipó, Itaquara, Nova Soure, Glória, Guajeru, Aracatu, Água Fria, Ubaíra, Varzedo, Sapeaçu, Jaguaquara, Sátiro Dias, Cravolândia, Governador Mangabeira, São Miguel das Matas, Muritiba, Irará, Cruz das Almas, Conceição do Almeida, Conceição da Feira, Urandi, São Gonçalo dos Campos, Mirante, Ribeira do Amparo, Manoel Vitorino, Antas, Paulo Afonso, Heliópolis, Coração de Maria, Jiquiriçá, Caetanos, Ouriçangas, Dom Macedo Costa, São Félix, Mutuípe, Jequié, Jacaraci, Santo Antônio de Jesus, São Felipe, Laje, Fátima, Pedrão, Santa Brígida, Presidente Jânio Quadros, Conceição do Jacuípe, Olindina, Sítio do Quinto, Maetinga, Cachoeira, Cocos
05Caetité
06Sobradinho
07Pojuca
08Pé de Serra, Riachão do Jacuípe, Nova Fátima
09Lagoa Real, Ibiassucê, Caetité
10Santa Cruz Cabrália
11São Francisco do Conde, Madre de Deus, Candeias
12Madre de Deus, São Francisco do Conde
13Maiquinique
14Santa Bárbara
15Itapetinga, Itororó
16Porto Seguro
17Ibipitanga
18Remanso
19Bonito
20Iuiu
21Vereda
22Cotegipe
23Itanagra
24Ituberá, Nilo Peçanha
25Esplanada, Cardeal da Silva
26Macajuba
27Várzea da Roça
28Presidente Dutra
29Catolândia
30Catu
31Baixa Grande
32Quixabeira
33São Domingos

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Figure 1. Centroids of spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model, according to the magnitude of relative risk. Bahia, Brazil, 2020–2022. Footnote: Clusters n° 01 to 23 = p < 0.0001; clusters n°s 24 and 25 = p < 0.05; clusters n° 26 to 30 = p ≥ 0.05.
Figure 1. Centroids of spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model, according to the magnitude of relative risk. Bahia, Brazil, 2020–2022. Footnote: Clusters n° 01 to 23 = p < 0.0001; clusters n°s 24 and 25 = p < 0.05; clusters n° 26 to 30 = p ≥ 0.05.
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Figure 2. Overlap of the spatiotemporal, high-risk clusters for COVID-19, identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022. Footnote: Google Earth Satellite View. Clusters n° 01 to 23 = p < 0.0001; clusters n°s 24 and 25 = p < 0.05; clusters n° 26 to 30 = p ≥ 0.05.
Figure 2. Overlap of the spatiotemporal, high-risk clusters for COVID-19, identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022. Footnote: Google Earth Satellite View. Clusters n° 01 to 23 = p < 0.0001; clusters n°s 24 and 25 = p < 0.05; clusters n° 26 to 30 = p ≥ 0.05.
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Table 1. Spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022.
Table 1. Spatiotemporal, high-risk clusters for COVID-19 identified using scan statistics with a Poisson model. Bahia, Brazil, 2020–2022.
Cluster IDNumber of Cities InvolvedSpan (km)Time FramePopulation at RiskNumber of CasesExpected CasesAnnual Cases per 100,000RR aLLR bp
1164586.32020/5/1–2021/6/30265,617,828702,720338,821.52226.82.8200,435.8<0.0001
2185596.82022/1/1–2022/2/28265,939,620102,53446,982.8238.72.2625,385.5<0.0001
3136460.72022/1/1–2022/2/28183,056,43666,09232,340.1223.52.0813,821.2<0.0001
4270940.72022/7/1–2022/7/31260,730,16834,45924,202.3155.71.431949.1<0.0001
51-2022/9/1–2022/10/311,933,3201139353.1352.83.23548.1<0.0001
61-2021/10/1–2021/11/30935,196477170.8305.42.79183.7<0.0001
71-2021/8/1–2021/8/311,228,764340114.13262.98145.4<0.0001
8330.52021/8/1–2021/9/302,045,316687373.6201.11.84105.1<0.0001
9342.42021/8/1–2021/8/312,851,284523264.7216.11.9897.9<0.0001
101-2021/10/1–2021/11/301,079,256399197.1221.42.0279.5<0.0001
11318.82022/11/1–2022/11/304,898,124729440181.21.6679.1<0.0001
12210.22022/7/1–2022/7/312,115,732392196.4218.32.0075.3<0.0001
131-2021/11/1–2021/11/30327,20411129.39413.03.7865.9<0.0001
141-2021/11/1–2021/11/30770,56818169.22286.02.6162.2<0.0001
15228.362021/11/1–2021/11/303,134,412488281.57189.61.7361.9<0.0001
161-2021/11/1–2021/11/306,109,620828548.83165.01.5161.3<0.0001
171-2021/8/1–2021/8/31519,60014248.23322.02.9459.6<0.0001
181-2021/9/1–2021/11/301,518,444642413.76169.71.5553.8<0.0001
191-2021/9/1–2021/9/30583,62013552.43281.62.5845.1<0.0001
201-2021/8/1–2021/8/31409,71610338.03296.22.7137.7<0.0001
211-2021/8/1–2021/8/31226,6567121.04369.13.3736.4<0.0001
221-2022/4/1–2022/4/30490,02011044.02273.32.5034.8<0.0001
231-2022/7/1–2022/7/31222,1686620.62350.03.2031.4<0.0001
24210.162022/11/1–2022/11/301,297,692193116.57181.11.6620.9<0.001
25213.702022/7/1–2022/7/311,547,040221143.60168.31.5417.90.003
261-2021/11/1–2021/11/30396,0727135.58218.22.0013.60.092
271-2022/11/1–2022/11/30512,6168546.05201.91.8513.20.132
281-2021/8/1–2021/8/31560,8809352.06195.41.7913.10.146
291-2022/11/1–2022/11/30126,8403211.39307.22.8112.40.233
301-2022/7/1–2022/7/311,832,664239170.12153.71.4012.40.243
311-2021/9/1–2021/9/30692,38810362.20181.11.6611.20.536
321-2022/11/1–2022/11/30349,3686131.38212.61.9410.90.613
331-2021/11/1–2021/11/30316,9685428.47207.41.909.10.99
a Relative Risk; b Log Likelihood Ratio. Bold represents statistically significant values.
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Saavedra, R.d.C.; Carvalho-Sauer, R.; Ichihara, M.Y.T.; Costa, M.d.C.N.; Soares, E.S.; Teixeira, M.G. Analysis of the Spatiotemporal Spread of COVID-19 in Bahia, Brazil: A Cluster-Based Study, 2020–2022. COVID 2025, 5, 109. https://doi.org/10.3390/covid5070109

AMA Style

Saavedra RdC, Carvalho-Sauer R, Ichihara MYT, Costa MdCN, Soares ES, Teixeira MG. Analysis of the Spatiotemporal Spread of COVID-19 in Bahia, Brazil: A Cluster-Based Study, 2020–2022. COVID. 2025; 5(7):109. https://doi.org/10.3390/covid5070109

Chicago/Turabian Style

Saavedra, Ramon da Costa, Rita Carvalho-Sauer, Maria Yury Travassos Ichihara, Maria da Conceição Nascimento Costa, Enio Silva Soares, and Maria Gloria Teixeira. 2025. "Analysis of the Spatiotemporal Spread of COVID-19 in Bahia, Brazil: A Cluster-Based Study, 2020–2022" COVID 5, no. 7: 109. https://doi.org/10.3390/covid5070109

APA Style

Saavedra, R. d. C., Carvalho-Sauer, R., Ichihara, M. Y. T., Costa, M. d. C. N., Soares, E. S., & Teixeira, M. G. (2025). Analysis of the Spatiotemporal Spread of COVID-19 in Bahia, Brazil: A Cluster-Based Study, 2020–2022. COVID, 5(7), 109. https://doi.org/10.3390/covid5070109

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