Infectious disease contact tracing is a crucial public health strategy for identifying and monitoring individuals who have been exposed to infectious pathogens.
This process involves tracing the contacts of infected individuals to rapidly isolate and quarantine potential secondary cases, thereby slowing the spread of the disease.
Contact tracing relies on thorough epidemiological investigations, including interviewing patients, analyzing travel and social histories, and utilizing digital technologies like mobile apps and location data.
Effective contact tracing can help contain outbreaks, prevent further transmission, and inform public health interventions.
Reseachers are continually exploring ways to optimize contact tracing through innovative approaches, including the use of artificial intelligence and machine learning to enhance tracing efficiency and accuracy.
By leveraging these advanced methodologies, public health officials can more effectively monitor and respond to infectious disease threats in our communities.
Most cited protocols related to «Infectious Disease Contact Tracing»
We included studies in any language of people with SARS-CoV-2 diagnosed by RT-PCR that documented follow-up and symptom status at the beginning and end of follow-up or investigated the contribution to SARS-CoV-2 transmission of asymptomatic or presymptomatic infection. We included contact-tracing investigations, case series, cohort studies, case-control studies, and statistical and mathematical modelling studies. We excluded the following study types: case reports of a single patient and case series in which participants were not enrolled consecutively. When multiple records included data from the same study population, we linked the records and extracted data from the most complete report.
Buitrago-Garcia D., Egli-Gany D., Counotte M.J., Hossmann S., Imeri H., Ipekci A.M., Salanti G, & Low N. (2020). Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis. PLoS Medicine, 17(9), e1003346.
Transmission dynamics follows a compartmental scheme specific for COVID-19 (Fig. 2), where individuals are divided into susceptible, exposed, infectious, hospitalized, in ICU, recovered, and deceased. The infectious phase is divided into two steps: a prodromic phase (Ip) occurring before the end of the incubation period, followed by a phase where individuals may remain either asymptomatic (Ia) or develop symptoms. In the latter case, we distinguish between different degrees of severity of symptoms, ranging from paucisymptomatic (Ips), to infectious individuals with mild (Ims) or severe (Iss) symptoms, according to data from Italian COVID-19 epidemic [18 ] and estimates from individual-case data from China and other countries [19 ]. We explore two values of the probability of being asymptomatic, namely pa= 20% and 50%, in line with available estimates [20 –22 ]. Individuals in the prodromic phase and asymptomatic and paucisymptomatic individuals have a smaller transmission rate with respect to individuals with moderate or severe symptoms, as reported by contact tracing investigations [23 ] and estimated in Ref. [8 ]. Current evidence from household studies, contact tracing investigations, and modeling works suggest that children are as likely to be infected by COVID-19 as adults, but more likely to become either asymptomatic or paucisymptomatic [22 , 24 –26 ]. This may explain the very small percentage (< 5%) of children in COVID-19 confirmed cases worldwide [27 ]. Here we assume that children in both classes (younger children and adolescents) are equally susceptible as adults, following Ref. [24 ], and that they become either asymptomatic or paucisymptomatic only. Different relative susceptibility or infectivity of children compared to adults is tested for sensitivity analysis.
Compartmental model. S, susceptible; E, exposed; Ip, infectious in the prodromic phase (the length of time including E and Ip stages is the incubation period); Ia, asymptomatic infectious; Ips, paucysymptomatic infectious; Ims, symptomatic infectious with mild symptoms; Iss, symptomatic infectious with severe symptoms; ICU, severe case admitted to ICU; H, severe case admitted to the hospital but not in intensive care; R, recovered; D, deceased
The compartmental model includes hospitalization and admission to ICU for severe cases. ICU admission rates, hospital case fatality, and lengths of stay after admission are informed from French hospital data for patient trajectories in Île-de-France region (SIVIC database maintained by the Agence du Numérique en Santé and Santé Publique France [28 , 29 ]) (see also Additional file 1). ICU beds’ occupation is the indicator used to evaluate the capacity of the region to face the surge of patients requiring intensive care. Since we do not use hospital beds’ occupation for this evaluation, we neglect the time spent in the hospital after exiting intensive care. Parameters, values, and sources used to define the compartmental model are listed in Table S1 of the Additional file 1 [8 , 18 , 19 , 21 (link), 28 , 30 –35 ].
Di Domenico L., Pullano G., Sabbatini C.E., Boëlle P.Y, & Colizza V. (2020). Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies. BMC Medicine, 18, 240.
Paired data of index case and close contacts were extracted from the contact tracing database and outbreak investigation reports. For a family cluster, the index case was determined based on the temporality of symptom onset and review of the epidemiological link. A secondary case was excluded from the paired data if the beginning of exposure was after symptom onset of the secondary case (only applied when the secondary case was symptomatic). For health care contacts, the date at exposure would be the date at admission of the case if the exact date at exposure was not recorded. Incubation period and serial interval were estimated using the contact tracing data in Taiwan and publicly available data sets globally (eMethods in the Supplement). We used the Bayesian hierarchical model to increase the stability in small-sample estimation. The exposure window period was defined as the period between the first and last day of reported exposure to the index case based on contact investigation. Following the WHO, we defined the secondary clinical attack rate as the ratio of symptomatic confirmed cases among the close contacts.19 We analyzed the dynamic change of secondary clinical attack rate in relation to symptom onset of the index case (days <0, 0-3, 4-5, 6-7, 8-9, or >9). The percentage of missing information was small (7.0% for age, 6.1% for sex, and 3.3% for time from onset to exposure; Table 1). In the univariable analysis of secondary clinical attack rate by different exposure characteristics (eg, age), close contacts with missing information in that particular exposure attribute were excluded. All statistical tests were 2-sided with an α level of .05. All confidence intervals (CIs) were 95%. R software (R Foundation for Statistical Computing) and RStan (Stan Development Team) were used for data management and analysis.
Cheng H.Y., Jian S.W., Liu D.P., Ng T.C., Huang W.T, & Lin H.H. (2020). Contact Tracing Assessment of COVID-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods Before and After Symptom Onset. JAMA Internal Medicine, 180(9), 1156-1163.
Close contacts of all adults aged ≥15 years with culture-positive pulmonary tuberculosis were prospectively enrolled in a multicenter study from January 2002 to December 2006 at 9 health departments (7 in the United States and 2 in Canada) in the Tuberculosis Epidemiologic Studies Consortium. Close contacts were defined as persons who had shared air space with an individual with pulmonary tuberculosis in the household or other indoor setting for >15 hours per week or >180 hours total during an infectious period, defined as the interval from 3 months before collection of the first culture-positive sputum specimen or the date of onset of cough (whichever was longer) through 2 weeks after the initiation of appropriate antituberculosis treatment. Contacts were screened as soon as possible after they were identified through interview of patients with tuberculosis and again 10–12 weeks after last exposure to the patient. Screening consisted of a standardized interview and tuberculin skin test (TST), with a positive TST result defined as a ≥5-mm induration. Chest radiography was performed for contacts with positive results of TST. While a standard protocol was used for conducting contact investigations, the staff at the study sites did not use a standard protocol for patient management, which included efforts to prevent secondary cases by investigation and treatment of contacts with LTBI. The Centers for Disease Control and Prevention’s (CDC’s) standard surveillance definitions for a reported case of tuberculosis were used for tuberculosis reporting by all study sites [6 ]. Contacts were cross-matched with state and provincial tuberculosis registries at the end of the enrollment period and annually for 4 years thereafter, with the exception of one study site, which cross-matched contacts annually for 2 years (the final match was in February 2011). The timing of tuberculosis among contacts was calculated by subtracting the tuberculosis diagnosis date for each index patient from the tuberculosis diagnosis date(s) for their contact(s), and tuberculosis rates per interval were based on the number of contacts with tuberculosis diagnosed in a given interval divided by the number of observed contacts who were disease free at the start of that interval. For contacts with exposure to >1 index case, the earliest index case tuberculosis diagnosis date was used. Tuberculosis events among contacts with disease diagnosed >30 days after the index cases’ diagnosis were considered incident cases, and tuberculosis events diagnosed before or ≤30 days after the index cases’ diagnosis were considered coprevalent cases. Survival analysis (Proc Lifetest) was performed using the log-rank test to assess the effect of age group, TST size, and preventive therapy on disease-free survival of contacts. Statistically significant differences for other analyses were assessed using χ2 or Fisher exact tests. All analyses were performed using SAS software, version 9.2 (Statistical Analysis Software Institute, Cary, NC). Approvals for human subjects research were obtained from the CDC and all project sites.
Reichler M.R., Khan A., Sterling T.R., Zhao H., Moran J., McAuley J., Bessler P, & Mangura B. (2018). Risk and Timing of Tuberculosis Among Close Contacts of Persons with Infectious Tuberculosis. The Journal of infectious diseases, 218(6), 1000-1008.
The TAM assessment focused on all field-based activities conducted by the CCG pairs from the time they left the clinic for household visits to the time they returned to the clinic. Our study team developed a standardized time reporting form based on reviews of CCG activity reports, discussions with CCGs, and direct on-site observations during a preceding pilot phase (January to March 2018). All participating CCG staff were trained to self-report TAM data during the study period. During each observation day, one member of the CCG pair recorded start and end times for each discrete activity (categorized based on a pre-defined set of activity codes shown in Table S1.1 in S1 Appendix) carried out by their peer in the field [16 (link), 17 (link)]. Times and activities were recorded in a continuous and consecutive manner with no time gaps between activities [17 (link)]. TAM data were collected in two distinct ways: at the household level and at the daily level. Household visit TAM forms captured the type and duration of activities performed within each household visit. Daily TAM forms captured information about the total time spent performing field work each day, the number of households visited, how much time was spent at patient households, how much time was spent traveling, and reasons for unsuccessful household visits. Both TAM forms were completed concurrently with each other, so data between forms could be linked. Direct activities were those that involved CCG service provision at households, namely household registrations, follow-up visits and other contact investigations (for TB and other diseases, such as maternal and child health and HIV). Indirect activities included travel from the clinic to the household, from one household to another, and back to the clinic. Each CCG pair was asked to complete both TAM forms at least three working days per calendar month during the study period, for an anticipated 264 form submissions. All submissions were voluntary and CCG pairs were not given specific days to collect data (i.e. submission and date selection was random). Completed paper-based forms were collected weekly by two trained study research assistants for data validation and were recorded into a Microsoft Excel database. The data validation process involved assessing the quality of each TAM form, based on the completeness and adherence to the time and activity reporting guidelines provided, with poor-quality forms excluded. Poor-quality forms were defined as forms that were not informative for TAM activities, such as forms with time gaps or missing information and those with suspicious patterns like all activities lasting for an equal duration. Feedback and quality reports were given to all CCG pairs to help improve quality of data collection over the study duration. Any data discrepancies were resolved by two independent reviewers reviewing the original data. Person-time–both in total and broken down per day, household visit, and patient interaction–were calculated and assessed for both clinics separately.
Mukora R., Thompson R.R., Hippner P., Pelusa R., Mothibi M., Lessells R., Grant A.D., Fielding K., Velen K., Charalambous S., Dowdy D.W, & Sohn H. (2023). Human resource time commitments and associated costs of Community Caregiver outreach team operations in South Africa. PLOS ONE, 18(3), e0282425.
The intervention consisted of a home-based, carer-enhanced, and individually tailored training program for safe gait aid use, which was delivered over six weeks. All study physiotherapists attended a three-hour training course delivered by the chief investigator and received a manual on the assessment protocol and principles of motor skill training based on errorless learning [22 (link)] to help participants achieve implicit learning for gait aid use (see Supplementary Materials S6). Each study physiotherapist visited their participants at their home four times, scheduled at week 1, 2, 3, and 6, to provide training for safe gait aid use. Informal carers were required to be present to observe the training sessions and followed written instructions provided by the study physiotherapists at each home visit to supervise the practice of gait aid use with the participant; the carers were also required to enable frequent and constant practice in between these scheduled visits if considered safe by the study physiotherapists. Regular contacts between the chief investigative team and the study physiotherapists were maintained to ensure compliance with the designed protocol during the study. The first home visit consisted of an initial assessment of the participant’s mobility and balance (as described above), a discussion with the participant and informal carer about their mobility requirements and preference of gait aid type, followed by a selection of the most suitable gait aid and adjustment of the gait aid to the participant’s height. All gait aids (either a 2- or 4-wheeled walker or a single-point stick) were purchased for the participants from a mobility equipment speciality supplier in Melbourne and Perth, Australia, and provided at no cost to the participants. Different types of gait aids were recommended and provided by the study physiotherapists for the training program. Fifteen participants (62.5%) were provided with a 4-wheeled walker, six (25%) with a single point stick, two (8.3%) with both a 4-wheeled walker and a single-point stick, and one (4.2%) with a 2-wheeled walker. Twenty participants (83.3%) changed from not using a gait aid to walking with the provided gait aid. Four (16.7%) changed from using a less supportive gait aid to using a 4-wheeled walker (Table 1). The training program allowed personalised variation as deemed appropriate by the study physiotherapists based on the mobility requirements (e.g., indoors, outdoors, step/kerb/stairs/ramp) and learning capacity of each participant. All visits typically consisted of approximately 30 min of training for safe gait aid use, focussing on the techniques of safe gait aid use and gait patterns for the participant’s mobility requirements.
Lee D.C., Burton E., Meyer C., Haines T.P., Hunter S., Dawes H., Suttanon P., Fullarton S., Connelly F., Stout J.C, & Hill K.D. (2023). The Potential for Effect of a Six-Week Training Program for Gait Aid Use in Older People with Dementia with Unsteadiness of Gait: A Pilot Study. Journal of Clinical Medicine, 12(4), 1574.
Data were collected from electronic medical records and contact investigation reports of the infection control team. Clinical, radiological, and laboratory data of the patients were obtained, including age, sex, comorbidities, previous TB history, symptoms, admission route, main diagnosis, department of admission, exposure days, AFB smear/culture, Xpert MTB/RIF assay, chest radiography, and/or chest computed tomography. TB exposure data, such as exposure site, route of close contact, HCW occupation, and previous TB history, were reviewed. Data on TB contact investigation included baseline/follow-up chest radiographic and IGRA results, newly diagnosed active TB or LTBI cases, and subsequent treatment. Delayed isolation was defined as the failure to isolate patients with active TB from the negative-pressure isolation room from the beginning of hospitalization. The exposure day was defined as the period from the time of patient admission to isolation. The radiologic findings of index patients were classified to simplified, suspected impression as follows: pneumonia if consolidation or ground-glass opacity was dominant, active TB if cavitation or tree-in-bud pattern was present, old TB if fibrotic scarring or calcification was present, and lung cancer if a mass or nodule was present. We devised a flowchart summarizing the processes of delayed isolation of patients with TB, and classified the patients into categories according to the patterns of delayed isolation. We analyzed these categories in-depth to identify the impact of delayed isolation on TB transmission.
Lee I., Kang S., Chin B., Joh J.S., Jeong I., Kim J., Kim J, & Lee J.Y. (2023). Predictive Factors and Clinical Impacts of Delayed Isolation of Tuberculosis during Hospital Admission. Journal of Clinical Medicine, 12(4), 1361.
After the identification of delayed diagnosis, the infection control team collected a list of HCWs suspected of having close contact with patients with TB without protective equipment. A pulmonologist was consulted to determine the proper range of contact investigation, based on clinical information, such as patient’s infectivity, environment, duration, and the procedure involved. HCWs were defined as those having close contacts with a patient infected by TB for more than eight consecutive or 40 cumulative hours, or those who participated in high-risk, aerosol-generating procedures (such as intubation, bronchoscopy, and suctioning) without protection equipment [17 (link)]. It is recommended that close contacts should be performed with chest radiographic and IGRA tests, using QuantiFERON-TB Gold In-Tube assay (Qiagen, Hilden, Germany) two times sequentially, immediately after exposure (baseline) and, then, at 8–10 weeks after exposure. If previous test results existed, chest radiographs within 1 month and IGRA test measurements within 6 months were considered baseline results. When the baseline IGRA result was positive, only a chest radiograph was obtained to rule out active TB. HCWs with a positive IGRA conversion were referred to a pulmonologist to consider active TB or latent TB infection (LTBI) treatment.
Lee I., Kang S., Chin B., Joh J.S., Jeong I., Kim J., Kim J, & Lee J.Y. (2023). Predictive Factors and Clinical Impacts of Delayed Isolation of Tuberculosis during Hospital Admission. Journal of Clinical Medicine, 12(4), 1361.
The study was embedded into routine contact investigations at primary care commune health posts and community-based active TB case finding (ACF) events. Details of the ACF events are provided elsewhere.27 (link) The study population included HHCs and close contacts, and vulnerable community members at elevated risk of active TB, such as the elderly, urban poor and economic migrants. Briefly, elderly persons were ≥55 years, urban poor were based on national poverty definitions and economic migrants were categorised based on residency registration in rural provinces outside of the intervention districts.28–30 (link) The HCMC site also included a subgroup of primary-level and secondary-level healthcare workers (HCWs) based on the request from local authorities. Recruitment and follow-up occurred from May 2019 to September 2020. All individuals presenting for screening provided routine demographic and clinical information including age, sex, residency status, history of TB, comorbidities and symptomatic presentation. Following intake, persons belonging to the study population with residency in the study districts were invited to participate in the study. Persons living outside of or intending to relocate away from the study sites, or who declined to consent were excluded. Eligible, consenting participants were recruited consecutively until the quota of available QFT-Plus tests was reached (n=5000 in HCMC and n=1000 in Hai Phong). Parents consented on behalf of their children under 18 years.
Vo L.N., Nguyen V.N., Nguyen N.T., Dong T.T., Codlin A., Forse R., Truong H.T., Nguyen H.B., Dang H.T., Truong V.V., Nguyen L.H., Mac T.H., Le P.T., Tran K.T., Ndunda N., Caws M, & Creswell J. (2023). Optimising diagnosis and treatment of tuberculosis infection in community and primary care settings in two urban provinces of Viet Nam: a cohort study. BMJ Open, 13(2), e071537.
Aged Child Health Personnel Hyperferritinemia, hereditary, with congenital cataracts Infectious Disease Contact Tracing Migrants Parent Primary Health Care Residency
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Infectious disease contact tracing is a multi-step process that involves identifying and monitoring individuals who have been exposed to infectious pathogens. The key steps include: 1. Interviewing infected individuals to gather information about their contacts, travel history, and social interactions. 2. Analyzing this data to identify potential secondary cases and map transmission chains. 3. Rapidly isolating and quarantining exposed individuals to prevent further spread of the disease. 4. Continuously monitoring those in quarantine for the development of symptoms. 5. Utilizing digital technologies like mobile apps and location data to enhance the tracing process and improve accuracy. By following these steps, public health officials can more effectively contain outbreaks and slow the transmission of infectious diseases within communities.
Implementing effective contact tracing can face several challenges, including: 1. Incomplete or inaccurate information from infected individuals due to poor memory, fear of stigma, or lack of trust in public health authorities. 2. Difficulties in tracking individuals who may have moved or traveled to different locations since exposure. 3. The need for rapid response and timely isolation of exposed individuals to prevent further spread. 4. Ensuring compliance with quarantine measures, especially among asymptomatic individuals. 5. Navigating privacy concerns and data security issues related to the use of digital technologies in the tracing process. To addrses these challenges, researchers are exploring innovative approaches, such as the use of artificial intelligence and machine learning, to enhance the efficiency and accuracy of contact tracing efforts.
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Researchers are continually exploring new and innovative approaches to enhance the effectiveness of infectious disease contact tracing. Some of these include: 1. Leveraging artificial intelligence (AI) and machine learning (ML) to automate and streamline the tracing process, improving efficiency and accuracy. 2. Integrating digital technologies, such as mobile apps and location tracking, to gather more comprehensive data on individual movements and contacts. 3. Developing advanced epidemiological models and simulations to better predict and respond to disease transmission patterns. 4. Exploring the use of blockchain technology to securely store and share contact tracing data while preserving individual privacy. 5. Investigating the potential of wearable devices and internet of things (IoT) technologies to continuously monitor and trace individual movements and interactions. By adopting these innovative approaches, public health officials can more effectively monitor and respond to infectious disease threats in our communities.
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Epidemiological Investigation, Infectious Disease Monitoring, Community Surveillance, Pathogen Exposure Tracking, Contact Tracing Methodologies, AI-Driven Tracing Optimization, Public Health Intervention Strategies, Outbreak Containment, Disease Transmission Prevention, Innovative Tracing Approaches, Mobile App Utilization, Location Data Analytics, Thorough Interviewing Processes, Social History Analysis, Travel History Review, Secondary Case Isolation, Quarantine Procedures, Digital Technology Leveraging, Metadescription: Discover how PubCompare.ai optimizes infectious disease contact tracing through AI-driven research protocol comparisons.
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