Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.1.1. Procedures
2.1.2. Ethics
2.2. The Network Scale-Up Method in the Literature
2.2.1. NSUM Questionnaire
2.2.2. NSUM Assumption
2.2.3. NSUM Estimator
- (1)
- Estimate the average size of the personal network, , by asking how many people the respondent personally knows about the k known populations (e.g., the number of people who were married in 2019). This number will then be divided by the number of people who got married in 2019 in Italy (), where is the total size of subpopulation k.
- (2)
- Define the number of hidden COVID-19 cases present in each social network, for example, by asking the respondent how many people he/she knows with COVID-19.
- (3)
- Calculate the COVID-19 population size obtained by multiplying the estimated proportions of the population in each subpopulation by the general Italian population. For example, if a respondent knows 10 subjects with COVID-19 cases and has a personal network of 100 people and the total population is 1,000,000, the estimated number of hidden COVID-19 cases will be approximately calculated as: 10/100 × 1,000,000.
2.3. Bayesian NSUM Estimation
2.3.1. Extended Random Degree Model
2.3.2. Performance of the Modified Maltiel Estimators
2.4. Statistical Analysis
3. Results of the Simulation Study
Performances of the Modified Maltiel’s Estimators
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author | Year * | N Known Population | Hidden Population | N Respondents | Method | Adjustment | Place |
---|---|---|---|---|---|---|---|
Ahmadi [34] | 2019 | 1 | Drug/alcohol users before driving | 363 | NSUM | Iran | |
Bernard [17] | 1991 | 6 | Deaths in earthquake | NSUM | Mexico | ||
Carletti [45] | 2017 | 20 | Oncological patients | 299 | NSUM | Italy | |
Ezoe [20] | 2012 | 3 | Men who have sex with men | NSUM | Transmission Error | Japan | |
Feehan [27] | 2016 | 22 | Populations at risk for HIV/AIDS | 4669 | Blended Scale-up | Rwanda | |
Guo [25] | 2013 | 3 | Populations at risk for HIV/AIDS | 2957 | NSUM | China | |
Habecker [51] | 2015 | 18 | Moved to Nebraska in US during last 2 years, do not approve of interracial dating, heroin users | 618 | Mean Of Sums NSUM | United States | |
Haghdoost [52] | 2015 | ** | Population of breast, ovarian/cervical, prostate, and bladder cancers | 3052 | NSUM | Iran | |
Heydari [40] | 2019 | 25 | Treatment failure | 2550 | NSUM | Iran | |
Jafari [28] | 2014 | 29 | Populations at risk for HIV/AIDS | 500 | NSUM | Transmission Bias, Barrier Effects | Iran |
Jing [23] | 2018 | 48 | Female sex worker | RRT, NSUM | Response Bias | China | |
Kadushin [38] | 2006 | 3 | Heroin users | NSUM | United States | ||
Kazemzadeh [53] | 2016 | ** | High-risk behaviors | 563 | CM, NSUM | Iran | |
Killworth [24] | 1998 | 24 | HIV prevalence, women who have been raped, the homeless | 1554 | NSUM | United States | |
Maghsoudi [33] | 2014 | 20 | Injection drug users, female sex workers | 600 | NSUM | Barrier Effect | Iran |
Maltiel [29] | 2015 | 29 | Populations at risk for HIV/AIDS | 500 | Bayesian NSUM | Transmission Bias, Barrier Effects | Brazil |
Mccormick [48] | 2010 | 12 | Personal network size | 1370 | Latent Non-Random Mixing Model NSUM | Transmission Bias, Barrier Effects, And Recall Bias. | Brazil |
Mohebbi [41] | 2014 | ** | People with disabilities | 3052 | NSUM | Iran | |
Moradinazar [54] | 2019 | ** | Suicides and suicide attempts | 500 | NSUM | Iran | |
Narouee [35] | 2019 | ** | Injection drug users | 1000 | NSUM | Barrier Effect | Iran |
Narouee [55] | 2020 | Rural area | 1000 | MLE—NSUM | |||
Nikfarjam [39] | 2017 | ** | Alcohol use | 12,000 | NSUM | Transmission Bias, Barrier Effects | Iran |
Nikfarjam [36] | 2016 | ** | Illicit drug users | 7535 | NSUM | Iran | |
Rastegari [42] | 2014 | ** | Abortions | 12,960 | NSUM | Transmission Bias, Barrier Effects | Iran |
Sajjadi [50] | 2018 | 6 | Students with high-risk behaviors | 801 | NSUM | Transmission Bias, Barrier Effects | Iran |
Salganik [30] | 2011 | 20 | Populations at risk for HIV/AIDS | NSUM, GSU | Transmission Bias, Barrier Effects | Brazil | |
Shokoohi [56] | 2010 | 6 | Network | 500 | NSUM | Iran | |
Shokoohi [31] | 2012 | ** | Populations at risk for HIV/AIDS | 500 | NSUM | Iran | |
Snidero [44] | 2012 | 33 | Foreign body injuries | 1081 | NSUM | Italy | |
Teo [26] | 2019 | 24 | Populations at risk for HIV/AIDS | 199 | Bayesian NSUM | Singapore | |
Vardanjani [57] | 2015 | ** | Cancer | 195 | Generalized NSUM | Iran | |
Wang [32] | 2015 | 22 | Men who have sex with men | 3097 | NSUM | China | |
Zahedi [37] | 2018 | ** | Drug users | 2157 | NSUM | Barrier Effect | Iran |
Zamanian [58] | 2016 | 25 | Age-gender distribution of women | 1275 | NSUM | Iran | |
Zamanian [59] | 2019 | 25 | Abortion | 1500 | NSUM | Barrier Effect | Iran |
Sub-Population of Known Size | Population Size | Reference Year | Source |
---|---|---|---|
People who separated | 99,611 | 2016 | Demographic model |
Foreign residents | 5,255,503 *** | 2019 | Demographic model |
Victims of car accidents with injuries | 3334 | 2018 | Demographic model |
People who graduated | 8530 ** | 2018 | MIUR |
People working part-time | 3,689,153 *** | 2019 | Demographic model |
Three-member families | 4954 | 2019 | AVQ |
Cohabiting couples | 14,110 | 2019 | AVQ |
People who married | 195,778 | 2018 | Demographic model |
Children born | 440,780 | 2018 | Demographic model |
People above 14 with smoking habits | 10,122 | 2017 | AVQ |
People using the mass media (newspapers, magazines, TV, radio, etc.) | 86,142 * | 2017 | AVQ |
People who attend places of worship | 14,264 | 2018 | AVQ |
People who walk to work | 2750 | 2018 | AVQ |
People who go to school by bus | 8743 | 2018 | AVQ |
Three-year-olds and above who used a PC and Internet | 62,232 | 2017 | AVQ |
Study Size | Maltiel’s Method | Modified Maltiel’s Method | |||||
---|---|---|---|---|---|---|---|
Benchmark Prevalence | Prevalence% | 95% CI Length | Bias | Prevalence% | 95% CI Length | Bias | |
1000 | 1.37 | 1.324 | 0.056 | −0.056 | 1.561 | 0.074 | 0.191 |
1500 | 1.37 | 1.327 | 0.044 | −0.053 | 1.57 | 0.056 | 0.200 |
2000 | 1.37 | 1.329 | 0.038 | −0.051 | 1.567 | 0.044 | 0.197 |
2500 | 1.37 | 1.329 | 0.031 | −0.051 | 1.566 | 0.038 | 0.196 |
3000 | 1.37 | 1.33 | 0.025 | −0.05 | 1.574 | 0.032 | 0.204 |
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Ocagli, H.; Azzolina, D.; Lorenzoni, G.; Gallipoli, S.; Martinato, M.; Acar, A.S.; Berchialla, P.; Gregori, D.; on behalf of the INCIDENT Study Group. Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol. Int. J. Environ. Res. Public Health 2021, 18, 5713. https://doi.org/10.3390/ijerph18115713
Ocagli H, Azzolina D, Lorenzoni G, Gallipoli S, Martinato M, Acar AS, Berchialla P, Gregori D, on behalf of the INCIDENT Study Group. Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol. International Journal of Environmental Research and Public Health. 2021; 18(11):5713. https://doi.org/10.3390/ijerph18115713
Chicago/Turabian StyleOcagli, Honoria, Danila Azzolina, Giulia Lorenzoni, Silvia Gallipoli, Matteo Martinato, Aslihan S. Acar, Paola Berchialla, Dario Gregori, and on behalf of the INCIDENT Study Group. 2021. "Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol" International Journal of Environmental Research and Public Health 18, no. 11: 5713. https://doi.org/10.3390/ijerph18115713