Disease Outbreaks

The epidemic curve was constructed by date of illness onset, and key dates relating to epidemic identification and control measures were overlaid to aid interpretation. Case characteristics were described, including demographic characteristics, exposures, and health care worker status. The incubation period distribution (i.e., the time delay from infection to illness onset) was estimated by fitting a log-normal distribution to data on exposure histories and onset dates in a subset of cases with detailed information available. Onset-to-first-medical-visit and onset-to-admission distributions were estimated by fitting a Weibull distribution on the dates of illness onset, first medical visit, and hospital admission in a subset of cases with detailed information available. We fitted a gamma distribution to data from cluster investigations to estimate the serial interval distribution, defined as the delay between illness onset dates in successive cases in chains of transmission.
We estimated the epidemic growth rate by analyzing data on the cases with illness onset between December 10 and January 4, because we expected the proportion of infections identified would increase soon after the formal announcement of the outbreak in Wuhan on December 31. We fitted a transmission model (formulated with the use of renewal equations) with zoonotic infections to onset dates that were not linked to the Huanan Seafood Wholesale Market, and we used this model to derive the epidemic growth rate, the epidemic doubling time, and the basic reproductive number (R₀), which is defined as the expected number of additional cases that one case will generate, on average, over the course of its infectious period in an otherwise uninfected population. We used an informative prior distribution for the serial interval based on the serial interval of SARS with a mean of 8.4 and a standard deviation of 3.8.^{11 (link)}Analyses of the incubation period, serial interval, growth rate, and R₀ were performed with the use of MATLAB software (MathWorks). Other analyses were performed with the use of SAS software (SAS Institute) and R software (R Foundation for Statistical Computing).

Based on the relevant literature—as described in the Literature Review section—we identified a few teacher characteristics that could be related to Self-Efficacy for Integrating Technology in Teaching and to Success in Teaching in ERT. Hence, we measured variables in a few categories, as described below. Descriptive statistics for these variables are reported in the Research Population section.
Demographic variables. Participants were asked to report on their Gender [Men, Women], Age (by reporting on birth year), and Mother Tongue [Hebrew, Arabic, Other]. Our participants included 599 females and 136 males (81% and 19%, accordingly), ranging in age between 21 and 69 years of age (M = 44, SD = 8.7, N = 735). These characteristics are largely aligned with the demographics of the Israeli teaching staff (Central Bureau of Statistics, 2020 ). Note that we assume normality for the age variable, as tests for skewness and kurtosis resulted with satisfyingly low values of 0.10 and − 0.31, respectively. Of the participants, 59% (437 of 735) reported that their Mother Tongue was Hebrew, 33% reported on Arabic (246 of 735), other languages were reported to relatively low degrees, hence we grouped them as “Other” (52 of 735, 7%); these ratios are slightly biased towards the Arab-speaking population, as teachers in the Arabic sector in Israel are about 22% of the overall teaching force at secondary education (Central Bureau of Statistics, 2021 ).
Teaching-related variables. We measured a few variables that helped us distinguish between teachers based on their professional characteristics. Specifically, we measured the following: Teaching Experience [years]; Experience in Teaching with Technology [5-point Likert scale]; Leading Role at School [yes/no for each of the following: grade coordinator, domain coordinator, ICT coordinator, counselor, vice principal, principal] – while processing the data, we aggregated this into a binary variable of managing position [yes/no]; Teaching Domain [Mathematics; Science; Technology; Language (mother tongue or second language); Humanities; Social Sciences; Arts; Physical Education; Other] – these values were chosen based on the way the Israeli curriculum is built, however while processing the data, and based on the responses, we defined only three categories: STEM (Science, Technology, Engineering, and Mathematics), Humanities and Social Sciences, and Language (either mother tongue of second language).
Our participants had an average Teaching Experience of 14.7 years (SD = 8.9, N = 735), with an average index of Experience of Teaching with Technology of 3.4 of 5 (SD = 1.1, N = 735). Note that we assume normality for these two variables; tests for skewness and kurtosis for Teaching Experience resulted with satisfyingly low values of 0.67 and − 0.34, respectively, and for Experience of Teaching with Technology they were − 0.10 and − 0.61, respectively. Of the participants, 32% (238 of 735) had a leading role at school, being part of the management team. Regarding their Teaching Domains, we had similar ratios of teachers teaching STEM (33%, 241 of 735), Language (either mother tongue or second language, 30%, 224 of 735), and Social Sciences or Humanities (37%, 270 of 735).
COVID-19-related variables. Finally, we measured a few variables that were unique to the COVID-19 pandemic outbreak. The variable Risk Group [Yes/No] indicates whether the participant or one of their household members were defined as being in a risk group for a severe illness from COVID-19; this group includes, among others, pregnant women, people over 60 years old, those who have a background of critical medical condition, and people who live in nursing homes. Additionally, we surveyed for four factors that were perceived as challenging working from home during the COVID-19 pandemic, each of which was ranked on a 3-point Likert scale: Physical Space Difficulties, Technology Difficulties (infrastructure-wise), Familial Difficulties, and Emotional Difficulties.
Of our participants, 24% (177 of 735) were in a Risk Group for COVID-19. Regarding the factors that influenced their working from home, Familial Difficulties where the most common (M = 1.96, SD = 0.75, N = 714), followed by Emotional Difficulties (M = 1.73, SD = 0.68, N = 708), Physical Space Difficulties (M = 1.633, SD = 0.73, N = 728), and finally Technology Difficulties (M = 1.627, SD = 0.73, N = 724).