|Year : 2021 | Volume
| Issue : 4 | Page : 123-132
Effectiveness of thermal screening for COVID-19: Some considerations
Colonel Rajiva1, Maninder Pal Singh Pardall2, Venkata A Kandukuri3, Saurabh Bobdey2
1 Tehri Hill Development Corporation, Uttarakhand, India
2 Department of Community Medicine, Armed Forces Medical College, Pune, Maharashtra, India
3 BTech Biotechnology, Vellore Institute of Technology, Vellore, India
|Date of Submission||25-Aug-2021|
|Date of Decision||25-Sep-2021|
|Date of Acceptance||28-Oct-2021|
|Date of Web Publication||15-Jul-2022|
Maninder Pal Singh Pardall
Department of Community Medicine, Armed Forces Medical College, Pune 411040, Maharashtra
Source of Support: None, Conflict of Interest: None
Introduction: Fever is a common symptom in most infections, and its rapid identification forms a major component of screening efforts. Such screening has been carried out by several countries during the SARS outbreak in 2003 and during the influenza A (H1N1) 2009 pandemic. Materials and Methods: Infrared scanner-based non-contact (IRSBNC) thermometer was used to measure the forehead skin temperature. A conventional mercury thermometer was used to measure the core body temperature. Verbal informed consent was obtained from all the study subjects. Data collection was unlinked and anonymous, thereby maintaining privacy and confidentiality. A large sample size of 414 study subjects was taken. Data so collected were subject to appropriate statistical tests. The same data were utilized to run a simulation-based Susceptible Exposed Infected and Recovered (SEIR) model regarding the percentage of infectors likely to escape thermal screening and its epidemiological impact using MATLAB software. Results: The mean forehead skin temperature of the study subjects as measured by an IRSBNC thermometer was 96.79°F. The mean oral temperature of the study subjects as measured by a conventional mercury thermometer was 97.33°F. The difference between the two means was statistically significant with a t-value of 8.16 (P < 0.01). The forehead skin temperature as measured by an IRSBNC thermometer and oral temperature as measured by a conventional mercury thermometer showed a poor correlation coefficient of 0.11. Sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of IRSBNC thermometer work out to 0.5384, 0.7087, 0.1102, and 0.9581, respectively. The false positive rate is 0.2912; and the false negative rate is 0.0309. The values of sensitivity, specificity, positive predictive value, NPV, false positive rate, and false negative rate of IRSBNC thermometer estimated using standard statistical tests are not much different from the results obtained by the simulation-based model. Conclusion: Based on the previous literature available and on the findings of the present study, which have been further validated by running a simulation-based model, the workers recommend that not too much reliance be placed on thermal screening by the IRSBNC thermometer. IRSBNC thermometers are of limited utility in thermal screening for Covid-19.
Keywords: Covid-19, negative predictive value, positive predictive value, sensitivity, specificity, thermal screening
|How to cite this article:|
Rajiva C, Singh Pardall MP, Kandukuri VA, Bobdey S. Effectiveness of thermal screening for COVID-19: Some considerations. D Y Patil J Health Sci 2021;9:123-32
|How to cite this URL:|
Rajiva C, Singh Pardall MP, Kandukuri VA, Bobdey S. Effectiveness of thermal screening for COVID-19: Some considerations. D Y Patil J Health Sci [serial online] 2021 [cited 2022 Aug 8];9:123-32. Available from: http://www.dypatiljhs.com/text.asp?2021/9/4/123/351082
| Introduction|| |
In December 2019, an outbreak of pneumonia of unknown etiology was detected in Wuhan, a city of 11 million people in Hubei province in Central China. The causative organism of this disease later was identified as the novel severe acute respiratory syndrome coronavirus 2, SARS-CoV-2. The World Health Organization duly acknowledged the alarming levels of spread and severity of the disease and labeled the COVID-19 situation as a pandemic on March 11, 2020.,
Having affected over 210 countries, COVID-19 has proved to be the largest pandemic experienced globally. Worldwide, over 2,403,963 individuals have been infected by the SARS-CoV-2, out of which 624,698,168 have recovered and 165,229 have succumbed to the disease. The main symptoms include dry cough, fever, fatigue, and difficulty in breathing. The disease has been observed to have a high morbidity rate in elderly population and in patients suffering from various other co-morbidities such as asthma, diabetes, cancer, and cardiac diseases.,
The rapid global spread of SARS from 2002 to 2003 prompted several countries around the world to assess the entry screening measures at their international borders extensively as one of the countermeasures to prevent the global spread of infectious diseases. An essential step in identification of cases who have SARS or avian influenza is detection of fever.
Detecting individuals who are in the incubation period or asymptomatic through entry screening is impossible. Hence, the efficacy of entry screening in correctly detecting and diagnosing influenza cases is likely to be small. However, entry screening was adopted to some extent by many countries during the early stages of the 2009 pandemic.,
Fever is a common symptom in most infections, its rapid identification forms a major component of screening efforts. Such screening has been carried out by several countries during the SARS outbreak in 2003 and during the influenza A (H1N1) 2009 pandemic.
The aim of this article is to run a simulation-based Susceptible Exposed Infected and Recovered (SEIR) model regarding the percentage of infectors likely to escape thermal screening and its epidemiological impact. This is to determine the effectiveness of thermal screening for Covid-19.
- (a) Discuss the characteristics of infrared scanner-based non-contact (IRSBNC) thermometer and its limitations;
- (b) Study the latent period, incubation period, serial interval, period of infectivity, and pattern of fever in Covid-19 cases;
- (c) Compare the difference between mean core temperature obtained in the study subjects using a conventional mercury thermometer and the forehead skin temperature using an IRSBNC thermometer;
- (d) Estimate the correlation coefficient between skin temperature obtained using the IRSBNC thermometer and core temperature obtained using a conventional mercury thermometer;
- (e) Estimate the sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of IRSBNC thermometer in detecting fever cases.
| Materials and Methods|| |
The workers carried out the present study in a presumably non-Covid-19 OPD-based health facility.
The study was carried out from the period May 2020 to Oct 2020.
The study was carried out in an urban area in western India.
Summer, monsoon (Rain), and onset of winter in India are the seasons.
The load of confirmed Covid-19 cases in the country grew over 200 times during the period of the study.
Participants and eligibility
Adults (>18 years of age) were considered. Subjects were approached after they had been registered in the organization from 7:00 am to 11:00 am, 7 days per week. They were enrolled in the study if they were willing to participate and gave verbal consent. Patients who were non-ambulatory, mentally incompetent, arrested or incarcerated, <18 years of age, or required immediate medical attention were excluded from the study. Pregnant women were excluded.
The IRSBNC thermometer was used to measure the forehead skin temperature. A conventional mercury thermometer was used to measure the core body temperature. There does not exist any gold standard for core body temperature.
HTC Instruments Model MTX-4.
Zero degrees selection for C and F
Laser target pointer selection
Automatic data hold function
Auto power off
White backlight LCD display
Over range indication
Type K temperature measurements
| Manufacturer Specifications: Qualification data|| |
Conduct of the study
Verbal informed consent was obtained from all the study subjects. Data collection was unlinked and anonymous, thereby maintaining privacy and confidentiality. Routine OPD cases reporting daily for minor ailments were included in the study.
Also determining a correction factor for oral temperature which would permit a more sensitive and specific detection of core body temperature is not possible. Hence, oral temperature of the study subjects was taken as an appropriate measure of core body temperature. Cut-off value for forehead skin temperature was taken as 36.25°C, i.e., 97.25°F. Cut-off value for oral temperature was taken as 37.5°C, i.e., 99.5°F.
The IRSBNC thermometer was held at an angle of 90°, 1 cm from the skin and the forehead skin temperature was measured twice. Mean of both these readings was taken as the reading for that study subject. This method of recording skin temperature using an IRSBNC thermometer has been validated by several workers.
NPV of IRSBNC thermometer has been reported to be of greater use when compared with positive predictive value and sensitivity. Sensitivity, specificity, positive predictive value, and NPV have been reported to be 89.4%, 75.4%, 33.7%, and 98.1%, respectively. NPV has been reported to be between 0.97 and 1.00 by Hausfater et al.
Thus, with 5% margin of error on either side and 95% confidence interval, the sample size was calculated to be 285. However, an even larger sample size of 414 study subjects was taken. Forehead skin temperature using an IRSBNC thermometer and oral temperature using a conventional mercury thermometer were recorded. These data were also used to compare the difference between mean oral temperature obtained in the study subjects using a conventional mercury thermometer and the forehead skin temperature using an IRSBNC Thermometer, as well as to estimate the correlation between skin temperature obtained using the IRSBNC thermometer and oral temperature obtained using a conventional mercury thermometer.
Data so collected were subject to appropriate statistical tests. The same data were utilized to run a simulation-based SEIR model regarding the percentage of infectors likely to escape thermal screening and its epidemiological impact using MATLAB software.
Latent period of Covid-19 has been reported to range from 2.56 ± 0.72 days.
Incubation period of Covid-19 has been reported by Mathias et al. to range from 4.8 to 5.1 days. Hong et al. reported the longest incubation period of 18 days and shortest of 0.08 days with a mean of 7.44 days. Roghayeh et al. reported an incubation period of 19 days in the case of a familial cluster. A maximum incubation period of 24 days with 3–7 days in most cases has been reported by Hua Li et al. Tak reported a mean incubation period of 8.5 days (95% confidence interval 7.8–9.2 days), median of 8.3 days (95% CI, 7.6–9.0 days), and an interquartile range of 4.9–12.0 days in 136 patients. On plotting against age with group size of 15 years, the incubation period showed a U-shaped curve with higher values at extremes of age. The mean/median incubation period for age groups 0–14, 15–29, 30–44, 45–59, 60–74, and 75–89 years was 12.0/13.5, 8.9/9.5, 8.4/8.5, 7.2/6.0, 9.6/9.0, and 13.2/13.0 days, respectively.
Juanjuan et al. reported a mean serial interval of 5·1 days (1·3–11·6) in their study on 8579 cases of Covid-19 from 30 provinces.
Period of communicability
Though SARS-CoV-2 is a mild infection with maximum lethality in elderly male population with pre-existing morbidities, it is contagious. This may be on account of the ability of the virus to transmit from subclinical patients. Literature search reveals reports of cases who have infected their close contacts even after “apparent recovery” from the infection. Chang et al. reported that 50% of patients remained SARS-CoV-2-positive for up to 8 days after resolution of symptoms.
Roghayeh et al. also reported transmission from asymptomatic patients during the prodromal period of Covid-19. Mathias Peirlinck et al. observed that the exposed individuals became infectious around day 3 of the exposure to SARS-CoV-2. The infectious period or period of communicability was 17.82 + 2.95 days. Infectious individuals become symptomatic around day 5, implying that they have potentially spread the disease for 2 days without being aware of it.
Juanjuan et al. also suggested the possibility of an early peak of infectiousness, with possible transmission of SARS-CoV-2 during the incubation period.
Pattern of fever
Fever has been reported in over 75–88% of cases at the time of admission. It has been reported more commonly in moderate cases than in mild cases.,, Some workers have also suggested that fever might not be the initial manifestation of Covid-19 and may be preceded by diarrhea and nausea for a few days prior to the onset of fever. Fever is usually less than 38°C in mild cases. Wen-Hsin et al. reported fever from 38°C to 39.1°C on days 6–9 of the illness. Alfonso et al. reported fever in 88.7% (95% CI 84.5–92.9%) of cases of Covid-19. They observed that frequency of fever was significantly higher in adults compared with children 92.8% (95% CI 89.4–96.2%), as against 43.9% (95% CI 28.2–59.6%) Covid-19 was observed to be present in most cases with a rapidly progressive course of fever. Similar findings have also been reported wherein 73% of pediatric patients were observed to have fever, vis-a-vis 93% of adults aged 18–64 years. Wang et al. reported fever in 97.2% of cases, lasting for about 10 days. They observed that fever was prominent and more severe in the fatal cases. The fever appeared in the first week of the illness and began to resolve in the second week.
Basic principles of infrared radiation
Sir William Herschel discovered infrared as a form of radiation beyond infrared light in the year 1800. Recording of the first thermal image by his son, John Herschel opened new dimensions in the field of temperature measurement. Infrared rays were used mainly for thermal measurement. They follow four basic laws of Physics, viz., Kirchoff’s law of thermal radiation, Stefan–Boltzmann law, Planck’s law, and Wien’s displacement law. Leopoldo Nobili used the Seebeck effect and fabricated the first known thermocouple in 1829.,
The Stefan–Boltzmann law gives the relationship between the emissive power of an object and its temperature. Infrared thermography (IRT) utilizes the wavelength of 8–15 nm in the infrared radiation band. The infrared energy emitted by an object is focussed by the camera on a detector, which in turn converts it into electronic signals for processing of images.
The physiological role of infrared emission from human body was first described by Hardy in the year 1934. He proposed that human skin can be considered as a blackbody radiator. The diagnostic importance of measurement of temperature by infrared technique was established. This further paved the way for the use of IRT in medical sciences.
Characteristics of infrared sensor-based thermal scanners
Infrared thermal detection devices provide a potentially useful alternative to contact thermometry and a non-invasive technique for monitoring temperature of any object above absolute zero. They have been used for screening for fever at hospitals and airports during the SARS and Influenza A (H1N1) 2009 pandemic.,
Hershler et al. carried out a study wherein they compared 30 measurements taken with a non-contact sensor, with readings obtained from a contact sensor. They observed that the measurements taken with the non-contact sensor were highly accurate and reproducible with a correlation coefficient (r = 0.9999).
Thermography has been found to have a significantly higher detection rate of febrile patients than that derived from a self-certification screening questionnaire. IRT has been reported to be a quick, passive, non-contact, and non-invasive alternative to conventional clinical thermometers for monitoring/assessing body temperature of human subjects.
The infrared camera detects radiation from different sources, viz., emission and reflected radiation from the object, emission from air and other objects. Approximately 86% of the radiation detected by an IRSBNC thermometer focussed at a distance of 1 m from a human being is emitted by the subject. The IRSBNC thermometer possesses a relatively high sensitivity and specificity. Hence, they have a high NPV in excluding non-febrile subjects.
| Results|| |
The mean forehead skin temperature of the study subjects as measured by an IRSBNC thermometer was 96.79°F (minimum 81.15°F, maximum 98.55°F, standard deviation 1.04). Mean oral temperature of the study subjects as measured by a conventional mercury thermometer was 97.33°F (minimum 95.4°F, maximum 99.9°F, standard deviation 0.91). The difference between the two means was statistically significant with a t-value of 8.16, P < 0.01. Forehead skin temperature as measured by an IRSBNC thermometer and oral temperature as measured by a conventional mercury thermometer showed a poor correlation coefficient of 0.11. Data of confirmed cases of Covid-19, recoveries, and deaths due to Covid-19 in India is tabulated in [Table 1]. Manufacturer specifications of the device are presented in [Table 2]. Results of the sensitivity, specificity, positive predictive value, and negative predictive value are tabulated in [Table 3].
|Table 1: Data of confirmed cases of Covid-19, recoveries, and deaths due to Covid-19 in India|
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|Table 3: Data of sensitivity, specificity, positive predictive value, and negative predictive value|
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Thus sensitivity, specificity, positive predictive value, and negative predictive value of the IRSBNC thermometer work out to 0.5384% or 53.84%, 0.7087% or 70.87%, 0.1102% or 11.02%, and 0.9581% or 95.81%, respectively. The false positive rate is 0.2912% or 29.12%; and the false negative rate is 0.0309% or 3.09%. Graph of data of confirmed cases of Covid-19, recoveries, and deaths due to Covid-19 in India is presented in [Figure 1]. Image of the IRSBNC thermometer which was used for data collection is depicted in [Figure 2].
|Figure 1: Graph of data of confirmed cases of Covid-19, recoveries and deaths due to Covid-19 in India. |
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Results of the variance of data using appropriate statistical tests and simulation-based model are presented in [Figure 3] and [Figure 4], respectively. Efficacy histogram obtained from the simulation-based model is presented in [Figure 5]. Results of the original data and simulation-based model which was carried out using MATLAB software are summarized in [Table 4]. From the above figures and tables, it is observed that the values of sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate of IRSBNC thermometer estimated using standard statistical tests are not much different from the results obtained by the simulation-based model.
|Table 4: Results of the original data and simulation-based model using MATLAB software.|
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- (a) A negative minimum deviation observed for false negatives (FF) and true negatives (TF) has been consistent in both data collected and simulated data. These cases are not a threat from an assessment perspective for the spread of the contagion.
- (b) The threat assessment for false positives (FT) is underscored by a higher mean deviation. This can vary from one thermal scanner to the other based on the specifications and results from the instrument qualification (install, process, output, and design qualification as laid out by the supplier).
- (c) The changes in climatic conditions/seasons had no direct co-relation to spread/containment of the contagion. Our sample size considered was more than the minimum calculated sample size; and the time interval during which the study was conducted was spread across three seasons. Temperature variations and humidity did not have a role in the spread/containment of the contagion. Details of the same are presented in [Figure 6].
|Figure 6: Temperature variations and humidity in the geographical area where the study was conducted Ref: Weather in June 2020 in Pune, Maharashtra, India (timeanddate.com).|
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Limitations of thermal scanners
Thermal scanners do not reliably detect cases of fever because of their low sensitivity and low positive predictive value, thereby indicating a high percentage of false positives. Outdoor temperature and age have been identified as confounding variables in cutaneous temperature measurement using thermal scanners. Elderly people display an impaired stability of core temperatures during cold and heat stresses and reduced cutaneous vascular reactivity. Because of the high rate of false positive results, mass detection of febrile patients cannot be envisaged by using this technique.
Nguyen et al. observed that infrared thermal detector was strongly influenced by site, time of day, and extended exposure to hot or cold environs. Front face readings by infrared scanners were also influenced by use of surgical facemasks and practice of opening the mouth. Screening for fever with the mouth open is contrary to good infection control practice and non-aesthetic. Distances at which infrared thermal scanners are currently deployed are very variable and imprecise. Less dependable estimates of core body temperature are forehead and other front face IRT. The infrared thermal scanners which are currently used for travelers at airports and border crossings are suspected. Standardization of various aspects, viz., location to be targeted, camera–subject distance, ambient temperature, and influence of exercise, are the main limitations of infrared thermal scanners which are currently in use.
The difference between the skin and core body temperatures needs to be corrected. The effects of environmental and ambient conditions and the various performance parameters of the thermal imager also need to be taken into account. IRSBNC thermometer has a reasonable accuracy in the detection of tympanic fever in the pediatric age group. However, it has a high false positive rate.
| Discussion|| |
Poor correlation coefficient of 0.11 obtained in our study is in contrast to the correlation coefficient of 0.99 obtained by Hershler et al. in their study. This could be because of increased skewing of data on our study due to hot Indian summers. Sensitivity and positive predictive value, obtained in our study, are much different from those obtained by Daniel et al. and Pierre et al. in their respective studies. Specificity and negative predictive value obtained in our study are quite like the results obtained by Daniel et al. and Pierre et al. in their respective studies.,
| Conclusion|| |
IRT provides a rapid, non-contact, and non-invasive alternative to conventional clinical thermometers for monitoring body temperature. A steady growth has been observed in the utility of thermal imaging cameras for the purpose of obtaining correlation between the human body thermal physiology and skin temperature in the last five decades. The advent of newer generation infrared detectors is causing infrared thermal imaging to become a more accurate medical diagnostic tool for the measurement of abnormal temperature pattern.
IRT is a harmless imaging technology, besides having better temperature sensitivity and non-contact nature. Thermal images obtained by IRT can be stored digitally and processed later using appropriate software for analysis of the thermal pattern.
Rapid, non-invasiveness, and accuracy should be the characteristics of an ideal device for fever screening. IRT is a potential tool which can be utilized for mass screening of fever. However, based on previous literature available and on the findings of the present study, which have been further validated by running a simulation-based model, the workers recommend that not too much reliance be placed on thermal screening by the IRSBNC Thermometer. Because of their high false positive rate, poor sensitivity, and abysmally low positive predictive value and poor correlation coefficient of forehead skin temperature as measured with IRSBNC thermometer with oral temperature as measured by a conventional mercury thermometer, and statistically significant difference between the two means with a t-value of 8.16 (P < 0.01), IRSBNC thermometers are of limited utility in thermal screening for Covid-19. However, due to high negative predictive value of thermal sensors which has been reported by several workers and validated by our study, the workers recommend that thermal sensors are more useful in ruling out the cases of fever than ruling in the cases of fever.,
The workers conclude that skin temperature measurement by IRSBNC thermometer is not a reliable basis for screening febrile patients. Mass detection of cases of fever with this technique should not be envisaged.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3], [Table 4]