Portal ENSP - Escola Nacional de Saúde Pública Sergio Arouca Portal FIOCRUZ - Fundação Oswaldo Cruz

Cadernos de Saúde Pública

ISSN 1678-4464

37 nº.3

Rio de Janeiro, Março 2021


Transmissão intradomiciliar em pessoas infectadas por SARS-CoV-2 (COVID-19) em Lima, Peru

Yolanda Angulo-Bazán, Gilmer Solis-Sánchez, Fany Cardenas, Ana Jorge, Joshi Acosta, César Cabezas


  • Artigo
  • Autores
  • Comentários (0)
  • Informações Suplementares

Transmissão intradomiciliar em pessoas infectadas por SARS-CoV-2 (COVID-19) em Lima, Peru

COVID-19; Busca de Comunicante; Serviços de Vigilância Epidemiológica



SARS-CoV-2 is an RNA virus belonging to the Orthocoronavirinae family, which also includes other causal agents of pandemics such as Middle East respiratory syndrome (MERS-CoV) and severe acute respiratory syndrome (SARS-CoV) 1. In Peru, the first case of COVID-19 was identified on March 6, 2020, and the first two deaths from the disease came 13 days later 2,3. Three months later, the country had passed 260,000 cases and reported more than 8,700 deaths (Ministerio de Salud. Sala situacional COVID-19 Perú. https://covid19.minsa.gob.pe/, accessed on 08/May/2020).

One of the most important characteristics of COVID-19 is its transmissibility dynamics, due to its highly effective transmission mechanisms. The infectious agent usually spreads by the respiratory route or contact with secretions. Thus, human-to-human transmission has become the principal route of spread to be managed in this pandemic 4. Previous studies have determined that SARS-CoV-2 has a mean basic reproduction number (R0) of 2.2, but the R0 can range from 1.4 to 6.5. The estimates can vary according to the study context 5,6.

Close contacts of cases such as family members, relatives, and friends are at the greatest risk of contracting the infection and can thus be sources of contagion for others in contact with them. This contagion chain is supported by the fact that a percentage of infected individuals can act as asymptomatic carriers of the disease, which hinders their identification by health systems 7.

An effective way to break the SARS-CoV-2 transmission chain is through epidemiological surveillance and follow-up of persons who were in close contact with a confirmed case 8,9. This process is called contact study or “contact tracing”. Some prior experiences have used these strategies to assess the transmission dynamics among household contacts of COVID-19 cases. A study in China reports a correlation between confirmed cases in other communities in Hubei province and the number of migrants in Wuhan, who usually came on family visits 10. Liu et al. 11 showed that family reunions became transmission hotspots in some provinces of China and thus recommended that public health interventions should consider specific measures to reduce contact in household members.

Other studies have found that secondary attack rates increase by 7-10 times when studying only the persons living in the same dwelling with the primary case, compared to the rate calculated when including all individuals in contact with the primary case 12,13. However, the evidence still differs between regions and countries where contact studies are performed.

In Latin America, deficient health systems and lack of economic resources add to the difficulty in tracing COVID-19 cases and contacts, which has proven to be an aggravating factor in the pandemic's progression 14. Benitez et al. 15, in a recent analysis in five Latin American countries, suggest that strict contact tracing, as in Chile, is associated with sustained decreases in COVID-19 cases. In addition, contact tracing has been implemented late in the region and is still incomplete in countries with high mortality rates like Brazil, Ecuador, and Peru 16.

Centralism is an additional factor in Peru. The capital and Greater Metropolitan Lima concentrate approximately 60% of all cases in the country. Within Greater Metropolitan Lima, districts have been identified with high and low proportions of cases and which have varied over time (Ministerio de Salud. Sala situacional COVID-19 Perú. https://covid19.minsa.gob.pe/, accessed on 08/May/2020).

Although a previous study was identified with a preliminary analysis of the SARS-CoV-2 transmission dynamics in Lima 17, no analyses were found of information on activities that involved follow-up of close contact clusters, that is, persons living in the same household, considering the COVID-19 burden by districts of residence. The current study thus aims to describe the characteristics of SARS-CoV-2 infection among members of households with a confirmed primary COVID-19 case in districts with low burden of cases in Greater Metropolitan Lima, compared to a district with high burden.

Materials and methods

Study design and type

The study has a quantitative design of the observational and retrospective type.

Population and sample

The study population was defined as all the reporting forms with the results of COVID-19 rapid lateral flow immunoassay. The inclusion criterion was contacts with complete epidemiological forms with IgG/IgM results performed by personnel from the Peruvian National Institute of Health (INS, in Spanish), included in household epidemiological surveillance. The sample excluded forms that were not found in the search process or that belonged to persons that did not live in the same household as the primary case. The study is thus defined as census type.

Epidemiological surveillance

In the context of the pandemic's control and surveillance, the INS conducted an epidemiological surveillance activity of households with a single primary case of COVID-19 (identified by RT-PCR) from April 23 to May 2, 2020. This evaluation was conducted on a mean of 13.6 ± 3.7 days following the primary diagnostic test.

In order to avoid the inclusion of COVID-19 cases from contagion in environments outside the household, the activity was conducted in districts with lower burden in each of the four descentralized health areas in Lima, called Integrated Health Network Directions (DIRIS, in Spanish), until reaching the surveillance of 10 households per DIRIS. This was performed by obtaining the results of molecular tests (RT-PCR) recorded since April 9 in the NetLab system, version 2.0, of the Peruvian Ministry of Health (https://netlabv2.ins.gob.pe/Login).

The results were grouped by district of residence and were ordered by burden of cases with each of their DIRIS, after which intentional non-probabilistic selection was used to pick 10 households from the districts with the lowest burden of cases in each DIRIS. In the DIRIS corresponding to the city center of Lima (with the highest population density), additional households were considered. The evaluation also included the district of Greater Metropolitan Lima with the highest proportion of cases at the beginning of the surveillance. A total of 52 households were included in the study.

Subsequently, as part of the surveillance, the 12 households located in the DIRIS corresponding to the city center of Lima were reevaluated, on average 33.6 ± 2.7 days after the first evaluation.

The serological test used was Coretests COVID-19 IgM/IgG Ab Test (Core Technology Co., Beijing, China), a lateral flow immunochromatographic assay that qualitatively detects the presence of antibodies to SARS-Cov-2, with sensitivity and specificity to IgM/IgG of 97.6% and 100%, reported by the manufacturer. These values were verified by the INS though evaluations at the laboratory level, reporting 96.4% sensitivity and 96% specificity for both IgG and IgM 18.


The study addressed a principal variable called SARS-CoV-2 infection and defined as the presence of antibodies (IgM, IgG, or both) in persons with no previous test result (RT-PCR or serological test). Positive cases were classified in turn according to the presence/absence of symptoms.

Information was also collected on the number of household members, evaluation time, defined as time in days between delivery of the index case result and the evaluation; and time with the disease, defined as time in days (patient-reported) from the onset of symptoms to the day of evaluation.

The study also described the sociodemographic characteristics of household members (age, sex, presence of healthcare workers), and clinical characteristics (presence of symptoms and risk conditions). Symptoms included cough, sore throat, nasal congestion, fever, general malaise, shortness of breath, diarrhea, nausea/vomiting, headache, irritability/confusion, pain in general, among others. Risk conditions included age 60 years or older, hypertension, cardiovascular disease, type 2 diabetes mellitus, obesity, asthma, chronic lung disease, chronic renal failure, immunosuppressive disease or treatment, cancer, pregnancy or postpartum, healthcare worker, or other conditions that the attending healthcare personnel consider relevant to record 19.

Statistical analysis

The descriptive statistical analysis of the data was carried out through determination of the frequency, percentage, mean, and standard deviation of the collected data. The evaluation was conducted differentially, expressing simple means for information from the subjects in general. Meanwhile, to identify the values for persons within each household, average measures were used, considering the variability existing in each household according to the density of members in it. The analyses were repeated for persons and households that were reevaluated in order to identify the changes occurred in time. The statistical software used was Stata version 16.0 (https://www.stata.com).

Ethical aspects

Since the current study used secondary data sources in the context of an epidemiological surveillance activity, no informed consent was required. The use of anonymous databases preserved the confidentiality of the participants' personal data, and no information was collected that would allow identification of the included persons. The study was approved by the Institutional Review Board of the INS (RD n. 256-2020-OGITT/INS).


We evaluated the records for 236 persons, of whom 54.7% (n = 129) were women, with a mean age of 36.2 ± 20.1 years. Mean time between detection of the primary case and evaluation of contacts was 13.6 ± 3.7 days. Some 37.3% presented a risk condition (n = 88), the most frequent of which was age 60 years or older (n = 35, 39.8%), followed by hypertension (n = 20, 22.7%) and bronchial asthma (n = 14, 15.9%). Of all the persons, 68.6% presented some sign and/or symptom, especially sore throat (49.4%), while fever and/or chills and cough were present in 41.4%.

Of all the sample, 53% were identified as secondary cases based on positive results in the lateral flow immunochromatographic assay, with 15 persons that were IgM-positive only, 110 that were IgM+IgG-positive, and none that were IgG-positive only. Among the secondary cases, 77.6% were symptomatic, and the symptomatic/asymptomatic ratio in secondary cases was 3.5 Table 1.



Tab.: 1
Table 1 Characteristics of persons evaluated in general. Lima, Peru.


Ages were similar between persons classified by serological results (positive/negative) and by symptoms (symptomatic/asymptomatic). 40.2% of symptomatic cases and 32.1% of asymptomatic cases had some risk condition, the most frequent of which was age 60 years or older. The most frequent signs and symptoms in the positive cases were fever and/or chills (40%), sore throat (39.2%), cough (35.2%), headache (30.4%), and general malaise (28%). Ageusia and anosmia were present in 22.4% and 20.8% of cases, respectively. The type of immunoglobulin detected was similar between symptomatic and asymptomatic secondary cases Table 2.



Tab.: 2
Table 2 Characteristics of persons with positive and negative results according to presence of symptoms.


The 236 persons belonged to 52 households, with a density of 4.5 ± 2.5 members per household. Considering the variability in the number of household members, 54.1% of the members were women, 34.7% had some risk condition, and 68.1% presented some sign and/or symptom. On average, 49.9% of household members were identified as secondary COVID-19 cases; of the 40 households with secondary cases, all the members tested positive in 9 (22.5%). On average, 39.4% of members were symptomatic secondary cases, and the symptomatic/asymptomatic ratio was 3.8 in secondary cases Table 3.



Tab.: 3
Table 3 Characteristics of the household composition (household members).


When evaluating the characteristics of households according to the members' test positivity, in those where all the members tested positive, 66.7% were women, while in those where all the members tested negative, this figure was 55%, compared to 50% where some members tested positive and others negative. The frequency of risk conditions was higher in households with more test-positive members Table 4.



Tab.: 4
Table 4 Characteristics of household composition (household members) according to the members' test results.


The reevaluation data referred to 40 persons distributed in 12 households, with an average age of 34.2 ± 17.2 years. Persons were reevaluated at 33.6 ± 2.7 days after the first evaluation. On average, 66.8% of the members were women, and 39.6% had risk conditions.

In the first visit, a mean of 1.9 ± 1.4 inhabitants per household had some sign and/or symptom (59.2%), while in the reevaluation this figure was 0.9 ± 0.5 (41.6%). In the first evaluation, there were 1.8 ± 1.5 positive cases per household (57%), while in the reevaluation this average was 2.0 ± 1.5 (65.6%). Thus, the ratio of positive cases in household members thus increased from 1.33 to 1.91. All IgM+IgG-positive individuals in the first evaluation were IgM+IgG-positive in the reevaluation.

The only case that was IgM-positive alone in the first evaluation was also IgG-positive in the second evaluation. In addition, three cases were identified that were initially negative and that tested positive for IgM and IgG in the second evaluation. The mean number of symptomatic positive cases per household in the first visit was 1.3 ± 1.4 (44.6%), compared to 0.8 ± 0.4 (37.4%) in the reevaluation. The ratio of positive symptomatic to asymptomatic cases changed from 3.60 to 1.33 Table 5.



Tab.: 5
Table 5 Variation in characteristics of persons in general and household members in the first evaluation and reevaluation.



This study found a secondary attack rate among household members of 53%, which is higher than in other studies that assessed SARS-CoV-2 transmission in similar clusters. The study that obtained the most similar results was by Wu et al. 20, evaluating 148 close contacts, all household members of a primary case, in China. Their evaluation found a 32.4% secondary attack rate (95%CI: 22.4%-44.4%).

Other studies in China, United States, and South Korea found secondary attack rates in household members ranging from 4.6% to 17%. Importantly, these estimates are affected by the sample size, ranging from 151 to 2,370 household members with confirmed cases 12,21,22,23. This discrepancy may be explained by social and cultural differences between the countries in which the studies were conducted, as well as between social distancing and quarantine measures applied by their respective states. No similar studies have been found in Latin America, so the true magnitude of the influence from these factors on the progression of COVID-19 transmission in households is not known.

Another explanation for these results is the time between detection of the primary and secondary cases, which in this study was an average of 13 days. Guan et al. 24, in follow-up of contacts living in the same household, found that 13 days after detection of the first case, more than half of the secondary cases had already been identified. Likewise, Qian et al. 25 found 88.8% detection of secondary cases in the same household, in contact tracing conducted in China.

The epidemiological characteristics of secondary cases in the current study showed a mean age of 36.1 ± 20.1 years, and 54.7% of cases were females. This distribution is consistent with the systematic review by Lovato & De Phillips 26, which found 42.5% of cases in males and a mean age of 49.1 years. It is also similar to reports by the Peruvian National Center for Epidemiology and Disease Prevention and Control (CDC-Peru), which mentioned that 59.9% of cases in Peru were 30-59 years of age and 41.8% were males 27.

Risk conditions were reported by 38.4% of positive cases, according to the prevailing definitions 28. The most frequent risk condition was age 60 years or older (18.4%), consistent with findings by CDC-Peru (17.3% of cases 60 years or older) 27. Davies et al. 29 also estimated that 69% of cases in older adults presented clinical symptoms, while susceptibility to the infection decreased to half in persons under 20 years.

Other risk conditions reported in secondary cases were hypertension (7.2%), bronchial asthma (6.4%), and diabetes (5.6%). Previous studies report divergent results on the frequency of cases of hypertension in COVID-19 patients, with figures ranging from 1.9% 30 to 17.4% 26, while presence of bronchial asthma ranged from 8.8%-12.5% 31,32, similar to the current study and reports in Greater Metropolitan Lima (18%-19%) in other studies 33,34. Finally, the frequency of type 2 diabetes mellitus in the current study is consistent with Tabata et al. 30, although higher than reported in other studies, in which the frequencies are 3% on average 26,35.

Meanwhile, the most frequently reported triad of symptoms was fever, sore throat, and cough, observed in approximately 40%-50% of positive symptomatic cases. These findings are consistent with previous studies evidencing that fever and cough were the most frequent symptoms, present in up to 80% of cases 26,36. Bi et al. 21 found a statistically significant relationship with a prevalence ratio of 3.06 (95%CI: 1.69-5.49) between fever and detection of COVID-19. While this study did not find a relationship between specific symptoms and test positivity (IgG and/or IgM), research on the natural history of this disease indicates that the appearance of IgM prior to IgG occurs during the first to second week after the onset of symptoms 37. However, in the current context, the appearance of suggestive signs and symptoms such as those mentioned should lead to a reasonable suspected diagnosis, with the decision to apply a confirmatory test.

In addition, 22.4% of symptomatic cases presented ageusia and 20.8% presented anosmia. The evidence is still not clear concerning the frequency of these findings in COVID-19 cases. On the one hand, some studies estimate their presence in more than 50% of cases 38. However, this is not backed by evidence from CDC-Peru, which reports 1.1% of anosmia and 0.3% of ageusia 27. There was an important potential information bias, given that these symptoms were not routinely investigated in cases. However, 92.9% and 90.3% of contacts with these symptoms tested positive for COVID-19 antibodies. Patel et al. 38 reported that 58% of household contacts in patients with anosmia and COVID-19 also reported symptoms of the disease and anosmia. Future studies should evaluate the characteristics better in relation to the appearance of these symptoms and SARS-CoV-2 transmissibility.

This study found 22.4% of asymptomatic positive cases, a rate similar to the 29% reported by CDC-Peru 27. This is also consistent with other contact tracing studies, such as Bi et al. 21, in China, showing 20% asymptomatic secondary cases and Cheng et al. 39, in Taiwan, reporting 18.2% asymptomatic secondary cases.

Meanwhile, reevaluation of cases concluded that although the proportion of persons with symptoms decreased, the ratio of positive cases increased from 1.33 to 1.91. This is consistent with the reevaluation time (more than 30 days on average), because the sensitivity of antibody detection in the population increases in proportion to the disease time 40. However, no major number of seroconversions was found in person that tested negative in the first evaluation.

This study also characterized households as measurement units, adjusting the epidemiological indicators according to the household´s density. This is important in a study of contacts in specific clusters such as dwellings, especially in a non-random selection mode, as in this case. In 23.1% of the households evaluated, no positive case was found; there was a mean density of 4.5 ± 2.5 persons per household and 3.7 ± 3.1 persons in households where all members tested positive. This density was similar and would not explain the absence of more infection in contacts in these households. Jing et al. 23 conducted a similar experiment and found 65% of households without positive cases, with a median of 6 (4,10) household members. However, they did not analyze the characteristics of households with positive cases.

In addition, and as expected, an important difference was found in the percentage of persons with symptoms in households with all positive contacts, compared to households with all negative contacts (83.6% vs. 48%).

As for differences between households in districts with low burden of COVID-19 cases and households in districts with high burden, in the former group, the weighted percentage of contacts with signs and symptoms and of contacts with positive test results was lower.

This study has some important limitations. First, the selection of households in the epidemiological surveillance activity was done by convenience, so the results cannot be extrapolated to the general population. There was no temporal component, which does not allow determining whether the cases called “asymptomatic” were in fact pre-symptomatic cases. The reevaluation activity could not be performed in all initially included households, which adds an important selection bias and decreases the external validity of the conclusions that can be obtained from these data. Even so, since no similar studies were found in Latin America, this study presents results that can serve as the basis for future studies that generate knowledge on SARS-CoV-2 transmission dynamics in households

Although the serological tests used in this study are backed by the official regulatory authority (INS) through laboratory assessments, in Peru there is only one field study that employs this type of test for this purpose. However, this test used another brand (Zhejiang Orient Gene Biotech Co., Huzhou, China) and did not define values for diagnostic yield.

Finally, the study concludes that with a primary case of COVID-19 in the household, the secondary attack rate for this infection was 53%. Still, in 23% of the households evaluated there were no positive cases beyond the primary case. The epidemiological and clinic characteristics found in this case agreed with reports in other international series. Likewise, the proportion of asymptomatic cases (22.4%) is consistent with evidence from previous publications and national epidemiological data in Peru. The study further evidenced the persistence of positive IgM in the reevaluation of cases 30 days later.


The authors wish to thank Drs. Joel Roque Hernández and Duilio Fuentes Delgado for their technical collaboration in the revision and suggestions during the process of the study protocol's approval. To the Peruvian National Institute of Health for financing the project. To Lilyana Collazos and Lenin Rueda for their technical support.


1.   Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 2020; 109:102433.
2.   Gobierno del Perú. Presidente Vizcarra dio a conocer primer caso de infección por coronavirus en el Perú e hizo un llamado a la población a mantener la calma. https://www.gob.pe/institucion/presidencia/noticias/86976-presidente-vizcarra-dio-a-conocer-primer-caso-de-infeccion-por-coronavirus-en-el-peru-e-hizo-un-llamado-a-la-poblacion-a-mantener-la-calma (accedido el 05/Abr/2020).
3.   Ministerio de Salud. Minsa lamenta el sensible fallecimiento de dos personas por infección por COVID-19. https://www.gob.pe/institucion/minsa/noticias/109603-minsa-lamenta-el-sensible-fallecimiento-de-dos-personas-por-infeccion-por-covid-19-comunicado-n-21 (accedido el 05/Abr/2020).
4.   Lake MA. What we know so far: COVID-19 current clinical knowledge and research. Clin Med (Lond) 2020; 20:124-7.
5.   Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med 2020; 27:taaa021.
6.   Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 2020; 25:2000058.
7.   Guo Y-R, Cao Q-D, Hong Z-S, Tan Y-Y, Chen S-D, Jin H-J, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Mil Med Res 2020; 7:11.
8.   Wang Y, Wang Y, Chen Y, Qin Q. Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. J Med Virol 2020; 92:568-76.
9.   Chih-Cheng L, Yen HL, Cheng-Yi W, Ya-Hui W, Shun-Chung H, Muh-Yen Y, et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): facts and myths. J Microbiol Immunol Infect 2020; 53:404-12.
10.   Fan C, Liu L, Guo W, Yang A, Ye C, Jilili M, et al. Prediction of epidemic spread of the 2019 novel coronavirus driven by Spring Festival transportation in China: a population-based study. Int J Environ Res Public Health 2020; 17:1679.
11.   Liu Y, Eggo RM, Kucharski AJ. Secondary attack rate and superspreading events for SARS-CoV-2. Lancet 2020; 395:e47.
12.   Burke RM, Midgley CM, Dratch A, Fenstersheib M, Haupt T, Holshue M, et al. Active monitoring of persons exposed to patients with confirmed COVID-19 - United States, January-February 2020. MMWR Morb Mortal Wkly Rep 2020; 69:245-6.
13.   Wu JT, Leung K, Bushman M, Kishore N, Niehus R, Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med 2020; 26:506-10.
14.   Burki T. COVID-19 in Latin America. Lancet Infect Dis 2020; 20:547-8.
15.   Benítez MA, Velasco C, Sequeira AR, Henríquez J, Menezes FM, Paolucci F. Responses to COVID-19 in five Latin American countries. Health Policy Technol 2020; 9:525-59.
16.   Moller S. Intervenciones sociosanitarias y uso de las tecnologías de la industria 4.0 para enfrentar la enfermedad por coronavirus (COVID-19) en América Latina y el Caribe. Santiago: Comisión Económica para América Latina y el Caribe; 2020.
17.   Munayco CV, Tariq A, Rothenberg R, Soto-Cabezas GG, Reyes MF, Valle A, et al. Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru, February 29th-March 30th, 2020. Infect Dis Model 2020; 5:338-45.
18.   Centro Nacional de Salud Pública, Instituto Nacional de Salud. Nota Informativa nº 072-2020-EQ-PR-COVID19. Lima: Instituto Nacional de Salud; 2020.
19.   Ministerio de Salud. Resolución Ministerial nº 193-2020, de 13 de abril de 2020. Prevención, diagnóstico y tratamiento de personas afectadas por COVID-19 en el Perú. Lima: Ministério de Salud; 2020.
20.   Wu J, Huang Y, Tu C, Bi C, Chen Z, Luo L, et al. Household transmission of SARS-CoV-2, Zhuhai, China, 2020. Clin Infect Dis 2020; 71:2099-108.
21.   Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis 2020; 20:911-9.
22.   COVID-19 National Emergency Response Center, Epidemiology and Case Management Team; Korea Centers for Disease Control and Prevention. Coronavirus disease-19: summary of 2,370 contact investigations of the first 30 cases in the Republic of Korea. Osong Public Health Res Perspect 2020; 11:81-4.
23.   Jing Q-L, Liu M-J, Zhang Z-B, Fang L-Q, Yuan J, Zhang A-R, et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect Dis 2020; 20:1141-50.
24.   Guan Q, Liu M, Zhuang YJ, Yuan Y, Wang SS, Li J, et al. Epidemiological investigation of a family clustering of COVID-19. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:629-33.
25.   Qian G, Yang N, Ma AHY, Wang L, Li G, Chen X, et al. COVID-19 transmission within a family cluster by presymptomatic carriers in China. Clin Infect Dis 2020; 71:861-2.
26.   Lovato A, De Filippis C. Clinical presentation of COVID-19: a systematic review focusing on upper airway symptoms. Ear Nose Throat J 2020; 99:569-76.
27.   Centro Nacional de Epidemiología, Prevención y Control de Enfermedades. Situación actual "COVID-19" al 21 de junio 2020. Lima: Centro Nacional de Epidemiología, Prevención y Control de Enfermedades; 2020.
28.   Ministerio de Salud. Resolución Ministerial nº 240-2020, de 29 de abril de 2020. Modifican el Documento Técnico: Prevención Diagnóstico y Tratamiento de personas afectadas por COVID-19 en el Perú aprobado por R.M. N° 193-2020-MINSA. Lima: Ministério de Salud; 2020.
29.   Davies NG, Klepac P, Liu Y, Prem K, Jit M, Eggo RM. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nature Med 2020; 26:1205-11.
30.   Tabata S, Imai K, Kawano S, Ikeda M, Kodama T, Miyoshi K, et al. Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: a retrospective analysis. Lancet Infect Dis 2020; 20:1043-50.
31.   Butler MW, O'Reilly A, Dunican EM, Mallon P, Feeney ER, Keane MP, et al. Prevalence of comorbid asthma in COVID-19 patients. J Allergy Clin Immunol 2020; 146:334-5.
32.   Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical characteristics of COVID-19 in New York City. New Engl J Med 2020; 382:2372-4.
33.   Robinson CL, Baumann LM, Gilman RH, Romero K, Combe JM, Cabrera L, et al. The Peru urban versus rural asthma (PURA) Study: methods and baseline quality control data from a cross-sectional investigation into the prevalence, severity, genetics, immunology and environmental factors affecting asthma in adolescence in Peru. BMJ Open 2012; 2:e000421.
34.   Lai CKW, Beasley R, Crane J, Foliaki S, Shah J, Weiland S, et al. Global variation in the prevalence and severity of asthma symptoms: phase three of the International Study of Asthma and Allergies in Childhood (ISAAC). Thorax 2009; 64:476-83.
35.   Nikpouraghdam M, Jalali Farahani A, Alishiri G, Heydari S, Ebrahimnia M, Samadinia H, et al. Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: a single center study. J Clin Virol 2020; 127:104378.
36.   Fu L, Wang B, Yuan T, Chen X, Ao Y, Fitzpatrick T, et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: a systematic review and meta-analysis. J Infect 2020; 80:656-65.
37.   Sethuraman N, Jeremiah SS, Ryo A. Interpreting diagnostic tests for SARS-CoV-2. JAMA 2020; 323:2249-51.
38.   Patel A, Charani E, Ariyanayagam D, Abdulaal A, Denny SJ, Mughal N, et al. New-onset anosmia and ageusia in adult patients diagnosed with SARS-CoV-2 infection. Clin Microbiol Infect 2020; 26:1236-41.
39.   Cheng H-Y, Jian S-W, Liu D-P, Ng T-C, Huang W-T, Lin H-H. Contact tracing assessment of COVID-19 transmission dynamics in taiwan and risk at different exposure periods before and after symptom onset. JAMA Intern Med 2020; 80:1156-63.
40.   Zhao J, Yuan Q, Wang H, Liu W, Liao X, Su Y, et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin Infect Dis 2020; ciaa344.

Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons


Cadernos de Saúde Pública | Reports in Public Health

Rua Leopoldo Bulhões 1480 - Rio de Janeiro RJ 21041-210 Brasil

Secretaria Editorial +55 21 2598-2511.

  • APOIO:

©2015 | Cadernos de Saúde Pública - Escola Nacional de Saúde Pública Sergio Arouca | Fundação Oswaldo Cruz. - Ministério da Saúde Governo Federal | Desenvolvido por Riocom Design