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Non-identifiable data that leads you or make you identifiable, even if ID and/or name are not provided, is called “Data Re-identification”. Those pieces of information become your fingerprint.

For example, if you verify your phone number with your passport or ID, it becomes your fingerprint. Government or telecom can know who you are.

You can identify me with these pieces of information.
– if we have a chance to meet all together, look for someone who is the most overweight with glasses, slow to speak, stay like a boss 😎

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Since quantitative survey reveals only how many people are using bed-nets or not, qualitative approaches will lead to find out the questions of why not using it, why using it. In this case, either in-depth interview or focus groups can be organized depending on the researcher’s conditions.

If researcher can manage individual interviews until data is saturated, in-depth interviews can be selected.
If research can organize and facilitate a group of people in a place(s) until data is coalescing, focus group can be chosen.

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Ease of use and usefulness are not directly or indirectly proportional to each other. It means that if a solution is easier, it represents neither more useful or less useful. Both key variables are in a combination such as Ease + Useful => Usage

So, if a old technology is replaced with a new one (more easier to use) while its usefulness is relatively the same, in individual perceptions, TAM model assumes that it will be more accepted to use.

However, if we consider it outside of individual perception, and view it from the organization perspective, the transition cost would be a burden while tech usefulness is the same.

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External variables apart from ease of use and perceived usefulness would be something that drives those two key variables (EOU, PU).
External variables could be very general, but it would be something that can adjust EOU and PU.

Digital literacy: So, if someone is not digitally friendly, EOU would be very low even though he finds it PU high.

Cost: If a tech solution provides high EOU, and PU but it is expensive, they will abandon to continue or start to use it.

Individual: Even if the solution is provided free and it is useful and easy to use, but if someone is not motivated to use it and prefer back to the previous solution (e.g., paper recording), they will not use it.

Support: If the solution is provided free, find it useful and it is made easy to use even for elderly, and people are motivated to use it, but if there is no training support, they will find it hard to use it.

In summary, external variables are facilitating factors that drive Perceived ease of use and perceived usefulness.

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Since the sole purpose of the experiment study is to test whether it works or not. The most important terms related to “works or not” are:

1. Efficacy – basically means “can (does) it work (under controlled conditions)?
for the first step of experiment, the research has to deal with testings under the ideal environment or conditions, e.g., new drugs under clinical trials
2. Effectiveness – means “does it work (really in the real world)?”
then, for example, weight loss pills are still effective even if the participants eat junk foods
3. Efficiency – means “is it worth (the cost and effort)?”
if the drug works, another concerns are: no. of doses required, before or after meal, medicine price, etc..

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Another potential is household composition. In association with age, in many cultures in ASEAN countries, young adults live with multi-generational families. So, this exposes to more contacts. When they get older, the move to a smaller composition of household with their small family, this exposes to less contacts within their house. Unless the contact tracing app has a feature to stop tracking at home, this would be a hidden potential factor.

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Infant Mortality Rate (IMR) is defined as the probability of a child born in a specific year or period dying before reaching the age of one, expressed per 1,000 live births (UNICEF, 2021).

IMR = (number of deaths under 1 year of age during a period / total number of live births during the same period) * 1,000

Main usefulness other than infant death rate,
– IMR is considered as a gold standard indicator because it reflects multi-faceted intersection of biological, social, and environmental factors. It reacts rapidly to changes in environmental sanitation, nutrition, and housing equality (Reidpath & Allotey, 2003).
– It provides a standardized metric for comparing the health status of different nations and regions (WHO, 2023).
– High IMR is frequently correlated with poverty and low educational attainment, particularly among women, making it a critical metric for international development agencies.

Reference:

Reidpath, D. D., & Allotey, P. (2003). Infant mortality rate as an indicator of population health. Journal of Epidemiology & Community Health, 57(5), 344–346. https://doi.org/10.1136/jech.57.5.344

World Health Organization. (2023). World health statistics 2023: Monitoring health for the SDGs. https://www.who.int/publications/i/item/9789240074323

UNICEF. (2021). Levels & trends in child mortality: Report 2021. United Nations Inter-agency Group for Child Mortality Estimation. https://data.unicef.org/resources/levels-and-trends-in-child-mortality-2021/

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Thank you for sharing interesting topics, Thinzar. I believe that WHO building blocks are different dimension from evaluating and standardizing. In my opinion, for community in conflict settings, the building blocks must be agile and flexible, modular (easy to integrate and disintegrate, easy to build and deconstruct, easy to move from one place to another). For example, for data capture application, it must support offline sync local first technology so that data entry can be performed while they reached out to places without any kind of telecommunication and networks. Only then, information block can become flexible and agile.
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Pragmatically, research priorities and types of questions asked or not asked depends on multiple stakeholders not only donor, including project or program manager/the organization who plan and execute the plan. It cannot be concluded as donor dependence or biases influenced by donors. I also wonder what kind of driven projects exist other than donor and external funding. Because donors and external funding deliver community healthcare services especially in conflict areas.

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Hello Wannisa, Thank you for sharing the interesting topics. Basic survey data such as age, sex, BMI, WC, BP are useful when advanced data (clinical data) are not available. However, basic survey data are one time data, cross-sectional data. This limits physiological judgment.
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Heterogeneous datasets would definitely introduce variations such as skewing differently in each cluster, for example, a country with older population will skew towards a cluster of highest mean age. So, It will affects age structure comparison. The survey year will help identify time trends if the model is applied to different years within the same country.

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It makes more sense to study with or without digital health reporting process as for supply side instead of before and after digital health intervention. Such evaluations will clarify the effectiveness of the intervention.
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Introducing a control group would help confirm whether the improvements in BMI and BP were truly caused by the health education and screenings in their study. I wonder how the study could be carried out with thousands households.

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This is new to me. Thanks for sharing new knowledge and insight. Physical presence when giving consent should be standard. Even if participants are in labor, researchers should humbly go to their workplace (if allowed) or wait outside, and explain in person, not forcing them to get the signature at once. This should be a standard way.
To the second question, why would someone in labor have time for a comprehension test? I strongly believe that it would be barriers to participation. Getting consent without physical presence should be the last option.

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1. What types of personal information are acceptable to collect to improve the performance and efficiency of digital contact-tracing apps? (For example: location data, Bluetooth proximity data, health status, or symptom reports.)

For the case of contact-tracing apps, no kind of personal information is acceptable to collect. If it is solely focused on the performance and efficiency of the app, then it only needs device information such as data sent timestamp, data received timestamp, app crash info, and so on.

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2. How would countries with low internet access or limited technology adapt digital tools for COVID-19 use?

Adapting tech for contact tracing is a significant challenge in regions with limited internet or tech. Instead, manual community-based strategies are more successful; for instance, in Myanmar, residents blocked off streets and established checkpoints to manually log and monitor the flow of people.

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Q1. Why is a multidimensional approach, such as the HVI mentioned in the study, more effective than conventional, single-factor measures of socioeconomic status in revealing the complex contextual health inequities?

A single factor concludes that one factor is associated with the effect. In reality, the effect is practically influenced by a wider range of contexts and factors. To understand complex contextual health inequities, it will be incorrect to blame a single factor for the effect. To capture a wider context, a multidimensional approach encompasses relevant facts to draw a reasonable conclusion about the cause.

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And which factors do you choose to have in HVI in your country?

For Myanmar, a traditional multidimensional approach will not be sufficient. Because in Myanmar, health is not about the hospital, but about safety. Other factors, such as conflict, should be considered.

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Q2. Considering that the goal of spatial data analysis is to guide decision-making, how should policymakers utilize the specific, measurable components of the HVI to design targeted primary prevention and intervention strategies aimed at reducing CP risk and socioeconomic inequalities?

Firstly, policymakers would like to know what the target population is, what the demand and supply of medicine and human resources required are, and how many resources are sufficient in reducing CP risk and socio-economic inequalities. This includes infrastructure improvement (sewage system, garbage collection), primary intervention focus (risks of low weight birth, maternal infections), etc.

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The intervention considered for my model is a COVID-19 vaccination campaign. During the pre-coup and post-coup periods, the only country that offers free access to the vaccine is India (manufactured CoviShield), despite having financial resources. Using the AstraZeneca (CoviShield) vaccine began in late January 2021, targeting health workers and high-risk groups (MOI, 2021). During the pre-coup, it showed a potential for reducing transmission and severe outcomes as perceived benefits (Simon & et. al., 2023). But following the February 1, 2021, the military coup led to widespread CDM participation by health professionals, suspending vaccinations by early February and healthcare collapse during the third wave. So, modeling this intervention directly ties to the effects of political stability but indirectly increases the transmission rate β.

To integrate the vaccination campaign into the SEIR model, I would additionally add the vaccination rate (V) that transitions from Susceptible (S) to Recovered (R).
So, the updated equations would be:

dS/dt = -β * (Susceptible * Infectious / N ) – Vaccination Rate * Susceptible

dE/dt = β * (Susceptible * Infectious / N) – σ * Exposed

dI/dt = σ * Exposed – γ * Infectious

dR/dt = γ * Infectious + Vaccination Rate * Susceptible

Characteristics of the intervention: As for efficacy of AstraZeneca, it shows approximately 70% in preventing symptomatic COVID-19 (Kohar et al., 2024). As for coverage, the proportion vaccinated daily is approximately 0.1% of the population initially targeting health workers and high-risk groups; after the coup, it was effectively collapsed.

Reference:

Ministry of Information, Myanmar. (2021). Announcement on launching of COVID-19 vaccination programme in Myanmar. https://www.moi.gov.mm/moi:eng/news/2586

Saw, S., Min, K. M., Latt, T. Z., & Tun, K. M. (2023). The community acceptance of COVID-19 vaccines in Rakhine State: A cross-sectional study in Myanmar. _PLOS Global Public Health_, _3_(8), Article e0002162. https://doi.org/10.1371/journal.pgph.0002162

Kohar, D., Das, B. K., Rawal, S., Kohar, B., Hussain, M., Yadav, D., & kajla, V. (2024). Evaluating the efficacy and safety of the Covishield vaccine: Balancing immune response and side effects in diverse populations. _World Journal of Biology Pharmacy and Health Sciences_, 20(2), 273-280. https://www.researchgate.net/publication/386276313_Evaluating_the_efficacy_and_safety_of_the_Covishield_vaccine_Balancing_immune_response_and_side_effects_in_diverse_populations

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Please feel free to comment on mistakes and flaws. Thank you.

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COVID-19 is a highly contagious respiratory illness. According to WHO, key characteristics include:
Transmission: Primarily air-borne via respiratory droplets or aerosols
Pathogenicity: Varies with age. Older individuals and those with underlying health conditions are at higher risk.
Symptoms: Includes fever, cough, and fatigue
Control measures: Includes vaccination, mask-wearing, social distancing, and frequent hand hygiene

Figure 1: Key time periods of COVID-19 infection: the latent or exposed period before the onset of symptoms and transmissibility, the incubation period before symptoms appear, the symptomatic period, and the transmissibility period, which may overlap the asymptomatic period. (Griette & Magal, 2021)

Among different model structures, the suitable one is the SEIR model (Susceptible-Exposed-Infectious-Recovered) because asymptomatic or pre-symptomatic transmission occurs during the incubation periods (WHO, 2020).

Table 1: COVID-19 Parameter Table

According to the WHO (2020), the basic reproduction number of the pre-coup period is between 2 and 4, while that of the post-coup period is between 0.5 and 1.7. According to the WHO, the incubation period is 5-6 days. For the general SEIR model, the infectious period is 5-7 days, for the recovery rate at 0.14-0.2 per day, and for the progression rate from Exposed to Infectious at 0.17-0.2 per day (Mawalili & et. al, 2020). The pre-coup transmission rate (β) is between 1.2-1.5 per day (Phyu & et. al., 2020). The post-coup transmission rate is expected to be higher than that, around 2.0.

In adjusting the R code, I will add an SEIR function and plotting with the population N=100 up to 365 days for pandemic simulation, including COVID-19 parameters in Myanmar:
β = 1.2 (pre-coup transmission rate)
β = 2.0 (post-coup transmission rate)
σ = 0.2 (progression rate from Exposed to Infectious as 1/5 incubation days)
γ = 0.1 (recovery rate)
Additionally,
dE will be β * [ (Susceptible * Infectious) / N ] - σ * Exposed

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Reference:

World Health Organization. (2020, April 2). Coronavirus disease 2019 (COVID-19) Situation Report – 73. World Health Organization. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200402-sitrep-73-covid-19.pdf

Griette, Q., & Magal, P. (2021). Clarifying predictions for COVID-19 from testing data: The example of New York State. _Infectious Disease Modelling, 6_, 273-283. https://doi.org/10.1016/j.idm.2020.12.011

World Health Organization. (2020, June 5). COVID-19 – a global pandemic. Retrieved from https://www.who.int/docs/default-source/coronaviruse/risk-comms-updates/update-28-covid-19-what-we-know-may-2020.pdf

Liu, Y., & Rocklöv, J. (2021). The reproductive number of the Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus. _Journal of Travel Medicine_, _28_(7), taab124. https://academic.oup.com/jtm/article/28/7/taab124/6346388

World Health Organization. (2020, April 2). _Coronavirus disease 2019 (COVID-19) Situation Report – 73_. Retrieved from https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200402-sitrep-73-covid-19.pdf

Mwalili, S., Kimathi, M., Ojiambo, V., Gathungu, D., & Mbogo, R. (2020). SEIR model for COVID-19 dynamics incorporating the environment and social distancing. _BMC Research Notes_, _13_(352). https://pmc.ncbi.nlm.nih.gov/articles/PMC7376536/

Phyu, W. W., Saito, R., Wagatsuma, K., Abe, T., Tin, H. H., Pe, E. H., Win, S. M. K., Win, N. C., Ja, L. D., Tsuyoshi, S., Makoto, K., Kyaw, Y., Chon, I., Watanabe, S., & Hasegawa, H. (2022). Epidemiology and Genetic Analysis of SARS-CoV-2 in Myanmar during the Community Outbreaks in 2020. _Viruses_, _14_(2), 259. https://pmc.ncbi.nlm.nih.gov/articles/PMC8875553/

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Please feel free to comment, including mistakes and flaws. Thank you.

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The disease I am personally familiar with would be Covid-19. I am interested in exploring the disease transmission and the impacts after the 2021 coup in Myanmar (country-level)

My research question would be: How do non-health factors (political instability, humanitarian aid blockage, etc.) affect the spread of virus and overall public health outcomes?

I think that modeling can answer this question by analyzing public health data against non-factor events over time.

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Q1. Why was the author interested in investigating the suicide problem in Thailand during that time?

The fact that the number of suicides has consistently ranged between 3,600 and 4,000 annually would draw noticeable attention to normal people. The author, including scholars, was interested in investigating the factors influencing the suicide problem in Thailand during that time since macro-level data on the topic for Thailand is limited and it has different economic and social characteristics than industrialized societies.

Q2. Picks one potential risk factor mentioned in the paper and explains how the variable can contribute to the suicide rate.

In the paper, a theoretical framework, the Utility Maximization Model, suggests that a person’s age and income influence their probability of suicide.

The question is, how is the divorce rate associated with the suicide rate, while the framework mentions only age and income?

One of the five assumptions delivered by the theory (Lester and Yang, 1997) is that divorce leads to the reduction of household income; therefore, such circumstances are more likely to commit suicide.

In the paper, the relationship between divorce rates and suicide rates is found to be statistically significant, with a coefficient of 0.508 showing a positive impact on suicide rates; meaning as the divorce rate increases, the suicide rate also tends to increase.

Q3. How can statistical modeling contribute to investigating the epidemiology and spatial aspects of the Thai suicide problem?

First, the author was interested in investigating the factors influencing the suicide rate in Thailand. So, to mathematically make a valid conclusion, statistical modeling helps to identify and quantify the impact of various factors on suicide rates across different places. By regression analysis, the author explored which socio-economic variables are statistically significant determinants of suicide rates.

To my understanding, spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors (Elliott & Wartenberg, 2004). The paper focuses on a multiple regression analysis to find correlations between these factors, rather than analyzing the geographical distribution or patterns of the disease.

However, in associating spatial aspects of the suicide problem, the study identifies that the northern region of Thailand had the highest suicide rates, while provinces in the northeastern, central, and Bangkok regions had lower rates. This helps to map out and identify specific areas that may require targeted interventions.

Reference
Prasertthai, S., & Panyathorn, J. (2019). Determinants of Suicide Rates in Thailand. *Journal of Community Development Research (Humanities and Social Sciences)*, *12*(1), 15-24. [https://doi.org/10.14456/jcdr.2019.2](https://www.google.com/search?q=https://doi.org/10.14456/jcdr.2019.2)

Elliott, P., & Wartenberg, D. (2004). Spatial epidemiology: Current approaches and future challenges. *Environmental Health Perspectives*, *112*(9), 998-1006. [https://doi.org/10.1289/ehp.6720](https://www.google.com/search?q=https://doi.org/10.1289/ehp.6720)

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Q1 (a). What are possible reasons locations in epidemiological research have not been incorporated as much as other components in epidemiological research?

Answer: The main reason could be the difficulties in correlating with places/locations in epidemiological research because chorographia or geography was not a part of epidemiological research. It is more likely that it was focused on person and time dimensions, for example, gender, age, occupation, socioeconomic level, disease trends based on a specific period, such as weather and climate. Other possible reasons could be a lack of technology support and data acquisition. It would not be convenient to collect data and tag locations on a paper map, and in the end, there would be challenges in performing clustering and correlation geographically. Therefore, locations were not much incorporated in the epidemiological research.

Q1 (b). How can spatial epidemiology be considered as an interdisciplinary science?

In discovering disease distribution patterns and the causative determinants among populations, focusing on locations, epidemiological knowledge is insufficient. Geography plays in understanding information about a place (such as weather, soil, air, and temperature). To infer the correct result from collected data, statistics helps with valid logical reasoning. To develop efficient analysis algorithms and visualization from large datasets, knowledge of computer science is required. Environmental science and public health also help identify environmental risk factors affecting disease patterns. Therefore, spatial epidemiology is inherently interdisciplinary and cannot function as an isolated field.

Q2 (a). Explain why it is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant, and give some examples?

Vector-borne diseases such as malaria and dengue are distributed in spatial patterns. Living in such regions could increase the risk.

People in remote rural areas lack health infrastructure, resulting in poorer health outcomes.

People living near or working in mines, landfills, and industrial complexes have a higher chance of lung cancer.

Therefore, it is widely recognized that the place where an individual lives or works should be considered as a potential disease determinant.

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What role does privacy play in user evaluations?

Not all users who rated the privacy aspects of the apps were satisfied with them. The majority of users expressed concerns about their privacy. It includes

– Excessive permission requests by software to function
– Concerns of data leakage and personal information security, such as unwanted SMS notifications for refunds on orders that were never placed.
– Unauthorized alteration of vaccine records, such as a child’s designated hospitals inexplicably being changed to another location.

In my opinion, these facts require a closer look.

First, unless requested permissions are given, does the software/app still function well? If it works well, it is excessive or suspicious.

Second, if unwanted SMS notifications are due to a technical glitch and happened once, this can be considered an unintentional mistake unless it falls into violation of privacy.

Third, from my perspective, alteration of designated vaccination sites seems acceptable, while it does not seem to be a medical record. It is more of a capacity or management issue.

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How did government restrictions affect mobility patterns?

The government restrictions include the closure of the friendship bridge, provincial entry and exit restrictions, with the establishment of checkpoints on main roads and a nationwide curfew.

It was introduced during Interval 4: 4 April 2020 to 3 May 2020 (30 days), the period of the most restricted travel.

The study measures the mobility in the radius of gyration (RoG) and observes the greatest reduction in movement, by more than 90% of the typical travel, by a 32% decrease in short trips outside the home village, and by a 70% decrease in long trips. Additionally, the nationwide curfew was effective, and the RoG was remarkably low during those hours.

The implementation of government restrictions during the pandemic led to a significant decline in mobility in their studied population.

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What factors influenced participant engagement in the study?

According to the paper, engagement from the web-based survey is higher than that from the mobile app. The platform is a major factor influencing the rate of engagement. In detail, responding to the survey from the app involves a series of steps such as downloading, account registration, activation, and so on. As for me, I won’t try spending these steps just for taking a survey, while participating in the survey already feels like a huge burden and a sheer waste of time.

A reminder also plays a crucial role in engagement in the study. Additional email reminders significantly improved survey completion. Psychologically, the reminder emails would remind them how important the survey is, and how important they are for the survey. If I were them, I would do the same, respect and respond to the survey.

The paper concludes that older adults have the lowest response rate among mobile app users, which is attributed to their lower technology literacy and discomfort with mobile apps. However, from the gerontological point of view, older people are not patient with complexity and longer waiting times. Additionally, normal people would not face barriers to the completion of the survey via the app. In my opinion, it is correct to say due to discomfort with mobile apps or user experience (UX) issues.

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How did apps affect vaccination uptake rates?

9 of the 28 studies assess how apps affect vaccination uptake rates. Only 4 studies reported a significant improvement in vaccination coverage.

Upon checking the apps from those 4 studies, they deal with immunization-related tasks such as immunization records associated with health workers and doctors. One unique app among them is Carrot Rewards, which enables users to earn loyalty points for completion of vaccination-related tasks.

Generally, regardless of these studies correlating with increased vaccination uptake rates, it can be said that apps do not affect the rate. Instead, the vaccination workflow requires such apps to streamline the process of vaccination records. Carrot Rewards is a different and innovative app that utilizes a brain-rewarding system to push users vaccinated.

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My first reaction to using the virtual space is that it’s something gamers would enjoy doing because it’s not different from a gaming environment. In sharing pictures or presenting in a shared virtual space, normally, we assume that sharing in a classroom or discussion board would draw more attention than virtual space because we have physical presence and human-to-human interaction, but the reality would seem to be the opposite. We rush to finish writing and reply to a discussion, or we could be daydreaming in a class during a lecture or presentation. In these ways, we already lose our attention. In the virtual environment, what we see is unavoidable, meaning that we become immersed in it and draw attention to everything.

In academics or professional settings, there can be situations where a virtual environment is required, for example, visualization of 3D objects – human anatomy, visualization of micro-molecules, flight simulation, and training. Just as a 2D picture is worth a thousand words, a 3D image is worth a million words. It will enhance and expand our understanding of different perspectives and enable us to see a wider picture.

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Thank you for sharing a thorough explanation. Any difference between the residual effects and the residual half-life of IRS?

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Thank you for sharing the HIV prediction model. If the duration of ART means adherence, it is a crucial factor for effective treatment, highly dependent on the patient’s consistent use of medication, which also reflects on the success of this intervention.

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Thank you for sharing a very insightful discussion. RSV dynamics and vaccination strategies are highly age-dependent, which is more realistic.

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Thank you for sharing the SIRV framework, which is a perfect way to model the impact of vaccination rate. Can vaccinated individuals be separated from the Susceptible compartment because they become infected?

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Thank you for bringing up an excellent point, Aung. My model’s parameter table incorrectly uses the incubation period to represent the transition rate (σ), but as you correctly pointed out, this is the latent period.

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Thank you, Ajan. I am considering scoping it down into, and could I change it to:

“How did the 2021 political instability of Myanmar affect COVID-19 mortality rates?”

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This sounds interesting, Alex. CVD receives insufficient attention within public health. Assessing the impact of intervention is expected to be complex, involving many factors like quantifying lifestyle changes. I am looking forward to seeing your answers to these research questions.

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Hi Tanaphu, thank you for sharing your interesting project. In computing methods, what kind of tools (e.g., R) are actually used to find the answers (estimate key parameters)?

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Thank you for sharing your discussion, Than. My curiosity was piqued about whether the finding would differ from other kinds of research designs, such as longitudinal analysis, cohort studies, and time-series analysis, when you mentioned it’s a cross-sectional analysis. Regarding spatial analysis, I am not sure whether adding location data as an independent variable of regression can be said to be spatial analysis. I think your response to the second question seems to be miscopied.

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As you discussed, I believe that divorce can bring serious mental complications in places where people consider marriage as a higher cultural value and societal expectation, as the paper mentioned. Statistical modeling, to be specific, two models of regression analysis are used for prediction and inference, by the relationship between the dependent variable (suicide rate) and the independent variables.

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I love the fact that you mentioned people in rural areas tend to have lower suicide rates due to their relationship (community support). Although such a strong social connection is not a risk factor, I agree that a lack of social connection would impose the risk.

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Thank you for your comment, Than. Data-driven public health responses without ecological fallacy are an effective solution.

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Thank you for sharing your valuable insight about how GIS / spatial clustering methods would help, Wannisa.

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Thank you for sharing your discussion. Regarding databases, to be more specific, historically, they lacked the functions of databases that offer storage, management, integrity, security, data retrieval, etc, for performing spatial epidemiological research. The lack of such technology support acted as a barrier to the continuation of further exploring spatial epidemiology.

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Thank you for sharing your discussion. I can certainly see that the place where we live and work can be regarded as a potential disease determinant. Even office jobs are exposed to eye strain, obesity, cardiovascular disease, back and neck pain, stress, burnout, forward head posture, etc.

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Privacy was one of the most dissatisfactory facts. Not only are developers responsible for it, but also an interdisciplinary team for user safety and privacy as a whole, such as a data protection officer and, product manager.

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The ability to access user data by developers is a significant concern. This is why the critical elements of any software are not its features, but the software firm’s strict adherence to data privacy and compliance standards, which technically prevents in-house engineers or anyone from accessing live data.

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I agree with your analysis. The dissatisfaction with privacy, despite good functionality, shows that user trust is important. To improve their apps, they have to work on them effectively.

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Thanks for sharing your opinion. It’s crucial to maintain a patient-centered, empathetic perspective, even when medical professionals are overwhelmed with their tasks. Instead of rescheduling the patient’s appointment with one click without notice, one call to explain the reasons for the change would be more appropriate and respectful.

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Thanks for sharing your thoughts. I agree that advance notification for appointment changes should be a required protocol.

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Thank you for sharing your discussion. I haven’t considered such an analysis by comparing travelers and non-travelers to provide insights for the imposition of stricter travel restrictions. As for me, since disease surveillance needs to be timely, I would suggest health screening monitoring, or requesting health declaration forms at points of entry from land, sea, and air, as soon as the initial evidence of a significant new threat, such as positive cases in neighboring countries, is received.

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It’s interesting to see that night work/life is still functioning (very little), although the night-time curfew led to a significantly decreased RoG value. When “curfew” is mentioned, I thought it’s unnecessarily serious because I am familiar only to “military martial law curfew”. I didn’t realize other kinds of curfew exists, e.g., public health curfew.

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The major barrier is a usability or experience issue, as you mentioned. If the app aims to be targeted at different kinds of audiences, it should have been designed with the principles of inclusive design, most importantly, age-friendly design.

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Yes, I agree with it unless emails are not as excessive as spam or marketing emails. Otherwise, we would pay attention to it.

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For me, it does not make sense if apps affect vaccination uptake rates because those apps are made to support the workflow of vaccination, such as digital records. Even if those apps also include a public health education feature, it’s public health education that increases the uptake, not necessarily the apps.

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For me, it’s a trick to say that it has a positive impact from 4 studies, while 9 studies assess it, because 5 studies are unknown.

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It’s interesting to see that the article mentions assumptions regarding older adults for the lower response rate. Despite these assumptions, as a young adult, I would not even try to waste my time on submitting a survey via the app.

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