em Português

    The present project intends to solve the problems related to modeling and decision-making on the occurrence of COVID-19 (Sars-CoV-2), offering a possibility of guidance for preventive public health policies. Therefore, we intend to investigate and make available methodologies based on official data and with high accuracy which allow to model and provide decision support for health managers on this public health problem. It is intended to publish partial results in a website providing to population and managers daily updates on this condition to increase epidemiological surveillance actions in order to combat COVID-19.



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COVID-19 in Brazil

COVID-19 in Paraiba state

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COVID-19 in Brazil up to November 28, 2021*

*Includes only data confirmed by the official platform of Brazilian Ministry of Health and from Brazilian Council of Health Secretaries (CONASS).

What is the Spatial Incidences Ratio?

The Spatial Incidences Ratio (SIR) presents the two incidences in a phenomenon occurrence, i.e., the local incidence with respect to incidence from all geographical region of interest. The first one presents the ratio between the number of cases computed in a subarea (in this case each state of Brazil) with respect to the population at risk in that same subarea. The second one presents the ratio between the total number of cases computed in all geographical region (in this case is the Brazil country), with respect to the total population at risk in that.

Formally, the SIR is given by the equation:

$$SIR(a_i)=\frac{\frac{V(a_i)}{X(a_i)}}{\frac{\underset{j=1}{\overset{n}{\sum <!--\; \sum \; -->}}V(a_j)}{\underset{j=1}{\overset{n}{\sum <!--\; \sum \; -->}}X(a_j)}}$$

where: a geographical region of interest is composed by n subareas denoted by $\{a_1,a_2,...,a_n\}$, which are the Brazilian states. The variable V is the number of cases registered in each one of those subareas $a_{\mathrm{i}}$ and the variable X is the population at risk in each one of those subareas.

The interpretation of SIR is simple and allows a direct comparison among the subareas $a_{\mathrm{i}}$. The table below shows its interpretation.

$SIR(a_i)=0$		There is no cases registered in that subarea
$0<SIR(a_i)<\mathrm{0,5}$		The SIR in that subarea is less than the half of geographical region incidence
$\mathrm{0,5}\le SIR(a_i)<\mathrm{1,0}$		The SIR in that subarea is greater or equal than the half and less than one times the geographical region incidence
$\mathrm{1,0}\le SIR(a_i)<\mathrm{1,5}$		The SIR in that subarea is greater or equal than one times the geographical region incidence, but does not exceed it by more than 50%
$\mathrm{1,5}\le SIR(a_i)<\mathrm{2,0}$		The SIR in that subarea is greater or equal than one and half times the geographical region incidence, but does not exceed it by more than two times.
$$SIR(a_i)\ge \mathrm{2,0}$$		The SIR in that subarea is greater or equal than two times the geographical region incidence

For more technical details about SIR and its applications to Spatial Epidemiology, please click on: Lima et. al. (2019a) (in Portuguese) and/or Lima et. al. (2019b) (in English).





The map of spatial analysis presents the centroids of states with different and significative values, from the statistical point of view, that compose spatial clusters. It means that neighboor states or single states with high values and with statistical significance compose a spatial cluster on the map. The methodology used was Spatial Scan Statistic. More technical details can be found at Lima et al. (2019b).

The map highlights in red the significant states. A large spatial cluster is composed by the states of Rio Grande do Sul, Santa Catarina, Paraná, Mato Grosso do Sul, Mato Grosso, Rondônia, Goiás, and Tocantins and Distrito Federal (Brazilian Federal District). A second spatial cluster is composed by the states of Espírito Santo e Rio de Janeiro in South-East of Brazil. The states of Amapá, and Roraima, are single spatial clusters.

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COVID-19 in Paraíba state up to November 28, 2021*

*Includes only data confirmed by the official platform of Brazilian Ministry of Health, from Brazilian Council of Health Secretaries (CONASS) and from Paraíba State Secretary of Health.


















The map of spatial analysis shows the centroids of municipalities from Paraí State with different and significative values, from the statistics point of view, that compose spatial clusters. It means that neighboor municipalities or single municipalities with high values and with statistical significance compose a spatial cluster of the map. The methodology used was Spatial Scan Statistic. More technical details can be seen at Lima et al. (2019b).

The map highlights in red the municipalities of Itabaiana, and Juripiranga, which compose a spatial cluster at South-East of the State of Paraíba. The municipalities of Solânea, Casserengue, and Algodão de Jandaíra compose a spatial cluster at East of the map. In the same region, the municipalities of Pilõezinhos, Guarabira, Alagoinha, Cuitegi, Alagoa Grande, Campina Grande, Barra de São Miguel, Boqueirão, Caturité, Riacho de Santo Antônio, Alcantil, Serra Redonda, Juarez Távora and Ingá compose other one. The municipalities of Rio Tinto, Marcação, Baía da Traição, compose a spatial cluster at North-East of the State. At north-Northeast, the municipalities of Caiçara, Tacima, Belém, Serra da Raíz, Sertãozinho, Lagoa de Dentro and Duas Estradas compose a spatial cluster. The municipalities of João Pessoa and Cabedelo compose a spatial cluster at East. At center-West, the municipalities of Catolé do Rocha, São Bento, and Brejo do Cruz compose a spatial cluster. At West, the municipalities of Piancó, Ibiara, Diamante, Itaporanga, Boa Ventura, Curral Velho, São José da Lagoa Tapada, Coremas, Pombal and São Domingos, compose a spatial cluster. At West, the municipalities of Triunfo, Santa Helena, Bom Jesus and Cajazeiras, compose a spatial cluster. At center-South, a spatial cluster is composed by the municipalities of Sumé and Monteiro. At the center-North of the State, São José do Sabugi, Areia de Baraúnas, São Mamede, Santa Luzia, and Patos compose a spatial cluster. Near them the municipalities of Frei Martinho, Picuí Nova Palmeira and Baraúna compose other spatial cluster. The municipalities of Parari and Santo André compose a spatial cluster at the central zone of the State. The municipalities of Aroeiras and Gado Bravo at center-South compose another spatial cluster. The municipalities of Carrapateira and Lastro (West), Seridó (at center-North), Livramento at Center of the State, Itapororoca and Esperança (at north-Northeast) and Mari (East) and Alhandra (South-East) are single spatial clusters.

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Team

• Ronei Marcos de Moraes (Coordinator - Departament of Statistics - UFPB)
• Liliane dos Santos Machado (Departament of Informatics - UFPB)
• Ana Cláudia Oliveira de Melo (Departament of Statistics - UFPB and PhD candidate in Decision and Health Models - UFPB)
• Claryce Rebeca de Souza Feitosa (scholarship PIBIC/CNPq/UFPB - Geoscience undergraduation student - UFPB)
• Luciana Moura Mendes de Lima (CAPES scholarship for PhD in Decision and Health Models - UFPB)
• Luiz Henrique da Silva (CNPq scholarship for Master in Decision and Health Models - UFPB)

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