COVID-19 epidemic curve in Brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological study

Pinto, Airandes de Sousa; Rodrigues, Carlos Alberto; Nascimento Sobrinho, Carlito Lopes; Cruz, Lívia Almeida da; Santos Junior, Edval Gomes dos; Nunes, Paulo Cesar; Costa, Matheus Gomes Reis; Rocha, Manoel Otávio da Costa

doi:10.1590/0037-8682-0118-2021

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Major Article • Rev. Soc. Bras. Med. Trop. 55 • 2022 • https://doi.org/10.1590/0037-8682-0118-2021 copy

COVID-19 epidemic curve in Brazil: a sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological study

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Background:

The epidemic curve has been obtained based on the 7-day moving average of the events. Although it facilitates the visualization of discrete variables, it does not allow the calculation of the absolute variation rate. Recently, we demonstrated that the polynomial interpolation method can be used to accurately calculate the daily acceleration of cases and deaths due to COVID-19. This study aimed to measure the diversity of epidemic curves and understand the importance of socioeconomic variables in the acceleration, peak cases, and deaths due to COVID-19 in Brazilian states.

Methods:

Epidemiological data for COVID-19 from federative units in Brazil were obtained from the Ministry of Health’s website from February 25 to July 11, 2020. Socioeconomic data were obtained from the Instituto Brasileiro de Geografia e Estatística (https://www.ibge.gov.br/). Using the polynomial interpolation methods, daily cases, deaths and acceleration were calculated. Moreover, the correlation coefficient between the epidemic curve data and socioeconomic data was determined.

Results:

The combination of daily data and case acceleration determined that Brazilian states were in different stages of the epidemic. Maximum case acceleration, peak of cases, maximum death acceleration, and peak of deaths were associated with the Gini index of the gross domestic product of Brazilian states and population density but did not correlate with the per capita gross domestic product of Brazilian states.

Conclusions:

Brazilian states showed heterogeneous data curves. Population density and socioeconomic inequality were correlated with a more rapid exponential growth in new cases and deaths.

Keywords:
COVID-19; SARS-CoV-2; Polynomial interpolation; Growth rate; Acceleration; Epidemic curve

States	PER CAPITA GDP	PD (N/km²)	GI	Total cases (n)	Total deaths (n)	IR (new cases/population *1000)	MR (new deaths/population *1000)	MCA (cases/day² )	PC (cases/day)	MDA (deaths / day²)	PD (deaths/day)	DD (deaths/ day²)
AC	17,636.74	5.45	0.6982	16,080	419	17.98	0.47	9.2	283	0.2	8
AL	16,375.55	120.37	0.7194	44,633	1,264	13.32	0.38	27.6	946	0.6	21
AP	20,247.28	6.05	0.7657	31,279	473	36.30	0.55	25.9	786	0.2	8
AM	24,532.85	2.63	0.8646	83,230	3,023	19.78	0.72	48.5	1,454	2.1	56	-1.41
BA	19,324.07	26.44	0.7831	104,188	2,436	6.98	0.16	101.3	3,159	1.0	52
CE	17,178.28	61.70	0.7883	135,945	6,853	14.80	0.75	52.5	2,711	3.2	124
DF	85,661.32	530.34	*	68,406	871	22.39	0.29	53.4	1,972	0.7	27
ES	34,493.11	88.21	0.7643	62,236	1,984	15.31	0.49	26	1,440	0.8	37
GO	28,273,00	20.91	0.7828	35,793	844	5.03	0.12	33.5	1,128	1.4	37
MA	13,955.68	21.58	0.7257	98,398	2,426	13.83	0.34	66.9	1,873	0.8	38
MT	39,931.17	3.90	0.7031	27,636	1,029	7.84	0.29	38.7	1,269	1.3	47
MS	38,925.80	7.87	0.6764	12,969	153	4.62	0.05	24.5	623	0.3	7
MG	29.223.23	36.30	0.8188	73,813	1,550	3.47	0.07	96.9	3,118	2	63
PA	18,952.26	6.98	0.7171	124,934	5,274	14.38	0.61	66.7	2,304	4.3	118	-4.3
PB	16,107.61	71.53	0.7788	60,421	1,250	14.96	0.31	34	1,204	0.4	25
PR	38,772.71	57.79	0.7727	40,797	1,016	3.54	0.09	65.2	1,649	1.6	44
PE	19,623.66	98.06	0.7878	71,370	5,556	7.42	0.58	29.1	1,198	2.4	86
PI	15,431.93	13.03	0.7639	32,465	914	9.89	0.28	35.6	1,252	0.5	24
RJ	44,222.66	396.94	0.8305	129,675	11,406	7.47	0.66	63	2,216	5.6	193	-3.7
RS	40,362.75	40.55	0.6764	38,720	943	3.39	0.08	30.8	1,135	1.5	38
RN	19,249.73	66.92	0.7887	38,616	1,380	10.93	0.39	36.8	1,140	1.3	39
RO	25,554.32	7.56	0.6877	26,496	617	14.75	0.34	17.6	597	0.4	13
RR	23,188.94	2.82	0.7396	21,849	396	34.62.	0.63	22.6	673	0.3	10
SC	42,149.28	75.76	0.7649	42,026	485	5.79	0.07	58.5	1,718	0.7	18
SP	48,542.24	186.49	0.8694	366,890	17,702	7.93	0.38	125.9	8,334	4.9	307
SE	18,442.63	105.76	0.7405	36,046	954	15.54	0.41	36	1,238	0.6	26
TO	22,932,96	5.73	0.7140	14,939	251	9.39	0.16	21.1	484	0.2	6

Tabela 1	GI r	Per capita	PD
	(valor-p)	GDP (reais)	(population / area)
Maximum case acceleration (casos/day²)	0.61 (0.001) ^*	0.16 (0.41) **	0.41 (0.04) *
Peak of cases (N)	0.66 (< 0.001) *	0.19 (0.35) **	0.53 (0.005) *
Maximum death acceleration (deaths/day²)	0.56 (0.003) *	0.28 (0.15) **	0.42 (0.03) *
Peak of deaths	0.63 (0.001) *	0.17 (0.40) **	0.49 (0.01) *
Incidence rate (new cases/population *1000)	-0.02 (0.94) *	-0.36 (0.06) **	-0,22 (0,26) *
Mortality rate (new deaths/population*1000)	0.25 (0.22) *	-0.30 (0.13) **	-0.02 (0.91) **

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[1] Corresponding author: Airandes de Sousa Pinto. e-mail:airandes@uefs.br