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I personally argue that in addition to determining suitable intervention programs, we also need to classify the targets of intervention because the susceptible populations (and the populations at risk) for mpox disease are specific. Thus, we cannot make a blanket claim that everyone is at risk.
In the context of the mpox epidemic in Indonesia, we highlight that mpox is considered a sexually-transmitted illness where the disease morbidity is high among the homosexual and bisexual groups but low in the heterosexual population. Here are the possible interventions we could apply to control mpox epidemic which could be included in the models:
To prevent people from becoming susceptible to virus exposure
– Interventions: health promotion with particular focus on chain transmission, including safe sex practice
– Characteristics of intervention: coverage, effectivenessFor the susceptible population, including those who may have been exposed:
– Interventions: two-dose mpox vaccination, integrated surveillance (HIV-mpox), contact tracing,
– Characteristics of intervention: coverage, vaccine efficacy, surveillance sensitivity, cost-effectivenessTo increase recovery rate and reduce fatality
– Interventions: intensive treatment and health facility-based isolation (not home isolation)
– Characteristics of intervention: coverage, drug efficacy -
I exhibit an SEIR (Susceptible Exposed Infectious Recovered) model below for illustrating the spread of Monkeypox infection among high-risk populations, using the presenting parameter derived from the paper presented by Lin et al (2023). Given the reproduction number R0 is 3.22, Sigma (infection period): 1/8.23 – 4, and Gamma (rate of progression from infection to recovery); therefore, the Beta variable is known as 0.9172577.
Although mpox has developed to become an epidemic in Indonesia in 2023, this model figures the initial spread of the disease progressing for 60 days ahead, assuming that there was 1 case in 100 high-risk groups.
https://snipboard.io/TvqSQi.jpg
R-code:
#Beta parameter given the reproduction number
R0 <- 3.88 #Reproduction number in a high-risk group
sigma <- 1/(8.23-4) #infection period, rate of progression from E to I is 8.23 − 4
beta <- R0 * sigma
print(beta)#Define the Parameter
par <- c(
beta <-0.9172577, #Human to human infection rate
gamma <- 1/21, #Rate of progression from I to R
sigma <- 1/(8.23 – 4) #infection period
)#Assuming the initial Condition
SEIR.init <- c(
S <- 100, #Number of High risk susceptible class of human population
E <- 10, #Number of Exposed class
I <- 1, #Number of Infected human
R <- 0 #Number of Recovered class of human population
)#Time Points
times <- seq(0, 60, by = 1) # Time span for simulation (days)# SEIR Model function
SEIR.dyn <- function(t, var, par) {
S <- var[1]
E <- var[2]
I <- var[3]
R <- var[4]
N = S+E+I+R
beta <- par[1]
gamma <- par[2]
sigma <- par[3]# Derivatives
dS <- -beta * S * I / N
dE <- beta *S*I/N – sigma*E
dI <- sigma * E – gamma *I
dR <- gamma * I
return(list(c(dS, dE, dI, dR)))
}# Solve the SIR model using differential equations
SEIR.sol <- lsoda(y = SEIR.init,
times = times,
func = SEIR.dyn,
parms = par)#Extract the results
SEIR.t<-SEIR.sol[,1] # Time
SEIR.S<-SEIR.sol[,2] # Susceptible population over time
SEIR.E<-SEIR.sol[,3] # Exposed population over time
SEIR.I<-SEIR.sol[,4] # Infected population over time
SEIR.R<-SEIR.sol[,5] #Recovery population over time#Plot the Model
plot(0, 0, type=’n’, xlim=c(0,60),ylim=c(0,120), xlab=’time’, ylab=’population’,
main = “Epidemic Curve Model of Monkeypox Disease in High-risk Group”)
points(SEIR.t,SEIR.S,type=’l’,col=’blue’,lwd=3)
points(SEIR.t,SEIR.E,type=’l’,col=’gold’,lwd=3)
points(SEIR.t,SEIR.I,type=’l’,col=’red’,lwd=3)
points(SEIR.t,SEIR.R,type=’l’,col=’green’,lwd=3)
legend(“top”, legend=c(“Susceptible”, “Exposed”, “Infected”, “Recoverd”),
col=c(‘blue’,’gold’, ‘red’,’green’), lty=rep(1, 3))Key characteristics of the mpox disease:
Transmission
– Description: mpox virus is mainly transmitted to humans from wild animals such as non-primate humans and rodents. However, transmission in Indonesia is known to be humans to humans, linked to contact with bodily fluids, a skin lesion on an infected individual, and respiratory droplets.
– Value or range: By the end of 2023, the total confirmed cases had reached 72 cases distributed throughout the 6 provinces where 98.6% cases were concentrated in Java, the most populous island.
– Source of information/data: Technical report of the mpox in Indonesia by Indonesia Ministry of Health, Peter et al (2022)Pathogenicity
– Description: Each case can have >1 coinfection and comorbidities, such as HIV, STIs (Syphilis, HSV), active TB, diabetes and hypertension. Confirmed cases can be asymptomatic, cases with mild conditions, and cases with severe conditions. Although mpox is considered a mild, it is a self-limiting disease.
– Value or range: The incubation period ranges from 1-21 days with an average incubation period of around 7 days. In mpox cases, the incubation period is calculated from the date of exposure until the onset of symptoms. In Indonesia, of the 69 symptomatic cases, only 28 cases had data on exposure to symptom onset.
– Source of information/data: Technical report of the mpox in Indonesia by Ministry of HealthSymptoms
– Description: The most frequently reported symptom is a lesion, which grows into several stages, namely vesicles, macules, papules and crusts. Lesions can grow on the face, feet, soles, genital area, mouth, entire body, chest, hands/palms, perianus and others (anus, head, neck, thighs, back, elbows and/or buttocks).
– Value or range: Of the 69 confirmed cases with symptoms (symptomatic) in Indonesia, the most frequently reported symptoms included lesions 100% (69 cases), followed by fever 86.9% (60 cases), rash 68.1% (47 cases), and lymphadenopathy 57.9% (40 cases).
– Source of information/data: Technical report of the mpox in Indonesia by Ministry of HealthControl measures
– Description: Communication, Information and Education are the main primary prevention measures carried out. Next, the mpox vaccination is given twice with a minimum span of 4 weeks from the first vaccine. In Indonesia, priority for the mpox vaccine is given to homosexual individuals who have had risky sexual relations in the last 2 weeks and to laboratory personnel who examine mpox samples.
– Value or range: Mpox vaccination is targeted at four administrative areas in DKI Jakarta, namely in Central Jakarta as many as 140 individuals, West Jakarta as many as 142 individuals, South Jakarta as many as 120 individuals and East Jakarta as many as 93 individuals with a total vaccination target of 495 individuals.
– Source of information/data: Technical report of the mpox in Indonesia by Ministry of HealthThe code and SEIR curve model will be provided below
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The choice of disease I would like to explore using mathematical modeling is the Monkeypox outbreak in Indonesia, an originally zoonotic infection transmitted from wild animals to humans but now occurring from humans to humans. Mpox has historically been a neglected zoonotic disease which mathematical models to explain its transmission dynamics are limited.
In Indonesia, the first confirmed mpox disease was reported in August 2022, involving a 27-year-old man with prior overseas travel. One year later on October 2023, Indonesia reported the second mpox case and started to massively conduct active case detection. By the end of 2023, the total confirmed cases had reached 72 cases distributed throughout the 6 provinces where 98.6% of cases were concentrated in Java, the most populous island.
Epidemiological investigation revealed an incubation period for mpox ranging from 1 to 21 days, with an average of 7 days. Among 72 cases, only one was female, identified as a spouse of a confirmed male mpox patient. Regarding sexual orientation, the outbreak was reported among 62,5% homosexual individuals, 23% bisexual individuals, and 10% heterosexual individuals. By 2023, mpox became comorbid among 73.6% of HIV patients. Clinical manifestation among all cases exhibited skin lesions and more than half of cases presented 6-25 skin lesions on the face.
Although mpox is considered mild, it is a self-limiting disease whose treatment and prevention control efforts indicate good progress. By the close of 2023, 63 patients had successfully recovered and only one fatality was reported, and the remaining cases were still under isolation. The Indonesia Ministry of Health executed a rapid risk assessment of mpox which the high-risk group targeting HIV patients and homosexual individuals in Jakarta. Following the case resurgence, the government has undergone two doses mpox vaccination with a primary emphasis on men who have sex with men (MSM) and laboratory officers who manage the specimens.
From the rationale above, research questions that may help us to understand the mpox situation using mathematical model are how the mechanism of transmission dynamics in Indonesia and how intervention methods such as two doses of vaccination can help reduce transmission in a high-risk group?
References:
https://infeksiemerging.kemkes.go.id/category/situasi-mpoxCitation:
Peter, O.J., Kumar, S., Kumari, N. et al. Transmission dynamics of Monkeypox virus: a mathematical modelling approach. Model. Earth Syst. Environ. 8, 3423–3434 (2022). https://doi.org/10.1007/s40808-021-01313-2Molla J, Sekkak I, Mundo Ortiz A, Moyles I, Nasri B. Mathematical modeling of mpox: A scoping review. One Health. 2023 Jun;16:100540. doi: 10.1016/j.onehlt.2023.100540. Epub 2023 Apr 17. PMID: 37138928; PMCID: PMC10108573.
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The decision to explore the place-related variables must be undertaken carefully and considers multiple factors such as the feasibility of data collection, the feasibility of data analysis, and data confidentiality.
1. Though the place-related variables can explain the dynamic pattern of disease transmission, this is often challenging to be included in the research milestones as it requires larger and more complex data rather than people and time variables.
2. Analysis of place-related variables requires specific skills and knowledge in geography and spatial analysis techniques. Spatial epidemiological research requires greater resources, especially in terms of technology and statistical expertise.
3. The issue of data confidentiality is also a consideration for researchers because the risk of disclosing an ‘area of concern’ can trigger public concern and has the potential to be a violation of data confidentiality.Spatial epidemiology is an interdisciplinary science because it covers various disciplines (public health, geography, statistics, and information systems) where there are intensive interactions between these sciences during the integration of concepts, methods, analysis, and knowledge synthesis.
It is true to recognize that place where people live is one of the determinants of diseases. The place variable can be a representation of environmental factors (which account for 40% of the health status) and healthcare facilities (which account for 10% of the health status), as described in the theory mentioned by Henrik L. Blum. The easiest example is the transmission of Dengue Haemorrhagic Fever, a vector-borne disease that occurs in urban areas, slum spots, and dense residential areas with high population density due to the impact of urbanization and human mobilization. People’s dependence on artificial clean water consumption may become a risk, particularly when their water management method risks becoming a breeding ground for mosquitoes.
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The researcher intended to summarize whether there is any association between socioeconomic factors that lead to suicide rates in Thailand. Models of these factors may have been made in previous studies conducted in developing countries, but the context is definitely distinct from Thailand where the vast majority of people work in the agricultural sector so the employment and income of Thai agrarians are included in the model. In addition, studies that collect the macro-level of data are rare. A previous similar study was conducted in Thailand, but it was outdated since this knowledge was generated, and it did not explore more specifically the factors of cultural, social, and economic differences among regions.
I would like to highlight one of the risk factors that is most associated with suicide rates, namely the percentage of elderly people (age above 60). The linear regression model produced a positive coefficient with a value close to 1 (almost perfect), which was interpreted that if the value of one variable increases, it is likely that the value of the other variables would also increase. In this case, provinces with a higher percentage of elders had higher suicide rates and it ascertains the previous descriptive analysis that explained the trend of increasing suicides among people age over 40 years.
Although the Thai suicide research did not explain causal relationships and did not predict trends of suicide in the future, statistical modeling with linear regression in epidemiological research explains the underlying mechanism of how predictors are associated with suicide problems as the outcome. With the multiple regression model, we can see the variation of the strength of independent variables to the outcome, as well as what is the association’s direction among the two variables. Back to the results about larger rates of suicide in provinces with a higher percentage of elderly people, this linear regression model helps spatial epidemiology to map the vulnerability of each province. The vulnerability map is useful for the government in prioritizing intervention program.
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I like that you raise a point about methodological complexity. Epidemiology in place variable, known as spatial epidemiology, contains a lot of complex statistical methods computed from either frequentism or Bayesianism (belief and probability) approach.
Spatial epidemiology allows us to display maps of disease risk that are leveraged for the decision-making process. These risk maps help identify areas with higher or lower disease risks, enabling policymakers to target interventions.
However, not all knowledge users, even decision-makers may fully realize and adopt the importance of statistical methods that work behind the map. When comparing different areas to find out high-risk distribution, people might use two or more common epidemiological measurements such as absolute numbers, rates, or proportions, then exhibit the number on a map to make assumption.
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If the smartphone camera can accurately detect breathing and heart rates, those rates can be used as supplementary biometric markers for enhancing smartphone security.
If this is so, the smartphones might need to be equipped with specialized sensors or gadgets to accurately measure these physiological parameters, such as pulse oximeter sensor, respiration rate sensor, and machine learning algorithm to process and analyse the data from the sensors that could differentiate between the user’s normal physiological patterns and potential anomalies.
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1) Many factors influence burnout syndrome in the workplace when it comes to jobs related to document records. Factors affecting burnout in the workplace may different between people, but I resume three critical aspects based on my point of view:
– Workload and staffing: this is an immediate cause as the workload must be in line with staffing support. Burnout appears when the workload is high but the staff number is inadequate.
– Lack of support, in terms of computerized infrastructure: when the supply of available IT facilities does not match our demands while we have to carry out tasks routinely, it can affect the mood of workers which leads to burnout.
– Perfectionism: some employees have this character, so they always ensure the process and results run as well as possible. Dissatisfaction may arise when processes or results, or both aspects are less than what they expect.2) The organization should ensure that the workload should be balanced with an adequate number of employees. To address the infrastructure issue, good planning and budgeting are important to support the run of work among employees. Encouraging support from colleagues, supervisors, and organizations to work as a team is needed to embrace all people to focus on progress, not perfection.
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This is a very good paper and you’ve presented it very well. In my opinion, the capacity at the points of entry to detect and prevent Covid-19 could be a potential parameter. Another potential indicator could be the density of airports/seaports/land borders in a country to the passengers. However, those parameters may be disproportionate as each entry point in a country has a different capacity level and different influx of travelers.
If I were the policymaker, of course, this risk stratification and assessment is one of the considerable steps. However, when deciding to disclose or keep close the border, other important socioeconomic aspects should also be considered as some areas in the country may have a strong dependency on economic activities and supply chains for food.
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Hi Siriphak. Same to Boonyarat, I was joining this forum when it was almost finished. My apologize. But I catched up some points in this paper.
While most kinds of the literature suggest that submicroscopic malaria (defined as low-density Plasmodium) is detected in the migrant population through travel activities from an endemic area, this research revealed that residing in the lakeshore contributes to vulnerability the most. I preferably argue that the male factor supports the continuity of SMM chain transmission as they have much more opportunities to go outside in the afternoon and back home at night while the home is around the lakeshore. To address the 2nd discussion, my opinion is human behavior. People tend to have much more activities in the dry season which shares a significant impact on the prevalence of Malaria.
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Hi guys, my dashboard is about Covid-19 in countries around the world using the Covid surveillance data. Please feel free to comment and share your idea to improve my dashboard appearance.
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The above picture illustrates a dashboard of Covid-19 situation in Jakarta, the capital of Indonesia that is a province. It is generated from a website that contains information about Covid-19 in Jakarta only. You can find this visualization at the following link: https://corona.jakarta.go.id/en/statistik-covid-19-berbasis-kelurahan .
The good things seen from this dashboard are:
1. Indicators used within this dashboard are quite familiar to people because it uses frequency, proportion, and rate measurements.
2. The dashboard is brave enough to display spatial data with a combined approach: polygons for areas inside Jakarta and satellites for areas outside Jakarta. The spatial unit displayed within the map shows the Covid-19 situation per village (pieces of sub-districts).
3. Not too crowded by colour as it consists of 3 different colours: white, yellow, and old-blue. Though the colouring used in the proportion diagram does not apply the brightness approach, the contrast between yellow and dark blue is easy to understand.In contrast, it’s disheartening to witness the weaknesses of this dashboard, including:
1. Bilingual option does not work for dashboard content but only works for website menus.
2. This visualization used less aesthetically pleasing template, seen from: a) disproportionate font sizes among indicators; b) formal colour in overall appearance creates the dashboard seems monotonous; c) the use of half-doughnuts for proportion diagram that is not appropriate.
3. Beneath map displays the rank of the high-risk region to the lowest one but uses a line graph. Also, information about the number collides with each other.
4. The colour differences on the map do not show specific information, only as a differentiator between one village and another.My recommendations for this dashboard are:
1. Dashboard’s contents should be able to be translated into various languages, at least Indonesian and English.
2. To overcome unattractive templates, the providers can: a) use chunking techniques to cut larger numbers or use proportional font sizes; b) selection of colors according to current trends; c) use a donut/pie chart/bar chart to display proportions.
3. Use a bar chart or table to rank areas with high to low transmission risk.
4. In mapping risks of cases, standardized epidemiological indicators (e.g., positivity rate) should be used to differentiate each spatial unit, while village boundaries should be sufficiently explained with lines. -
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The decision to explore the place-related variables must be undertaken carefully and considers multiple factors such as the feasibility of data collection, the feasibility of data analysis, and data confidentiality.
1. Though the place-related variables can explain the dynamic pattern of disease transmission, this is often challenging to be included in the research milestones as it requires larger and more complex data rather than people and time variables.
2. Analysis of place-related variables requires specific skills and knowledge in geography and spatial analysis techniques. Spatial epidemiological research requires greater resources, especially in terms of technology and statistical expertise.
3. The issue of data confidentiality is also a consideration for researchers because the risk of disclosing an ‘area of concern’ can trigger public concern and has the potential to be a violation of data confidentiality.Spatial epidemiology is an interdisciplinary science because it covers various disciplines (public health, geography, statistics, and information systems) where there are intensive interactions between these sciences during the integration of concepts, methods, analysis, and knowledge synthesis.
It is true to recognize that place where people live is one of the determinants of diseases. The place variable can be representation of environmental factors (which account for 40% of the health status) and healthcare facilities (which account for 10% of the health status), as described in the theory mentioned by Henrik L. Blum. The easiest example is the transmission of Dengue Haemorrhagic Fever, a vector-borne disease that occurs in urban areas, slum spots, and dense residential areas with high population density due to the impact of urbanization and human mobilization. People’s dependence on artificial clean water consumption may become a risk, particularly when their water management method risks becoming a breeding ground for mosquitoes.
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I would like to provide an argument about the misinterpretation of number 11, and it may be related to number 12 (on page 342).
“P = 0.05 and P ≤ 0.05 mean the same thing.”
In social, public health, or other non-exact research, most investigators set probability values as ≤ 0.05. In order to avoid redundancy in grammar, they prefer to write ‘equal to 0.05’ and ‘less than 0.05’ in one unit to become P ≤ 0.05, rather than using the concept of writing P =0.05 and P< 0.05 in a straightforward manner and is separated. Writing down these probability values using the style as ≤ 0.05 affects any misinterpretation, whereas the exact P value number should have its own position and honor and it cannot be generalized immediately that for example P =0.04 is the same as P< 0.05. Researchers tend to orient their perspective on cut-off values because that’s where conclusions can be drawn, rather than believing that the smaller the P-value, the stronger the evidence. This misinterpretation may occur when drawing conclusions, but it will be even more fatal if the number is reported as inequality when the research result is being written.
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Following the Technology Acceptance Model (TAM) 2 framework that added and incorporated social influence and cognitive instrumental processes that affect perceived usefulness, designing intervention that would drive intentions of use is an excellent option in order to create usage behavior. This way helps decide how an individual within an organization responds to the new technology, whether they will replace old technology with a new one or not.
Intervention approaching social influence considers how the closest people or important people who have an image expose their influence to adopt new technologies and leave old ones. The exposed influence could be their experience using new technology and voluntarily suggesting how useful the new one is. This influence resulted in social pressure for someone to follow adopting new technology in order to be as same as the figure in their social group.
For cognitive instrumental processes, I argue that this approach is an evidence-based process, therefore, longitudinal intervention is needed until it is proven that the new technology can demonstrate the expected result. According to TAM 2, cognitive instrumental processes will lead to perceived usefulness, so there is an interactive effect between job relevance, output quality, and result demonstrability in determining perceived usefulness.
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My non-identifiable data are listed below:
1. Sex: Male
2. Nationality: Indonesian
3. Current location: Jakarta
4. Blood Type: O
5. Ethnicity: Java
6. Education: SKM (Bachelor of Public Health)
7. Occupation: Epidemiologist
8. Workplace: Directorate General of Disease Prevention and Control, Ministry of Health Indonesia
9. Year of enrolment in the workplace: 2018
10. Previous Education Institution: Universitas Diponegoro
11. Place of Birth: Tegal City, Central Java
12. Current Education Institution: Mahidol UniversityIf that information is being filtered, data from numbers 1-10 will still show many people with the same characteristics. But starting from 11-12 is definitely me.
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In case the quantitative study revealed that the vast majority of people did not use their bed nets to prevent mosquito bites, then, the qualitative method I need to undertake comprises multiple approaches and is under sequence way in order to answer the research question deeply and comprehensively.
First, I need to undergo a participant observation to indirectly situate the environmental and social causes, as well as find other questionable conditions that need to be confirmed in the further methods. By this observation, I probably will make sense of the hot temperature of the region, the poverty situation that is seen by building contour, the way people set their beds which enables bed nets are difficult to mount or their habits about sleeping outside the house. Those variables will not directly lead to the conclusion but will be explored and confirmed under the second sequence approach by conducting a Focus Group Discussion (FGD).
The FGD I would like to execute involves at least two or three different groups where one group consists of five to six participants. Subjects come from previous quantitative research, so I will consider the previously filled-out questionnaire to do selection and hold recruitment for FGD. FGD allows me to gain a wide variety of participants’ reasons and exchange viewpoints among people, as well as discuss agreements or disagreements between people. Moreover, FGD enables allow cross-reading results to confirm the different directions of answers among groups. I will probably get reasons about uncomfortable when sleeping under a mosquito net, mosquito nets blocking the wind, not being able to buy a mosquito net, assumptions about mosquito density being low, or the same answers as observation results.
The analysis of FGD will combine both condensing and displaying techniques. It is important to transcript the audio into the next and not publish its original audio or video to maintain information confidentiality and for participants’ safety purposes. Transcription enables me to outline the quotes and summary of notes. FGD results might be explorative where reasons answering why people did not apply the bed nets can be figured out using comparative word cloud or bulleted summaries. On other hand, participants may also present a causality between reasons in which the mind mapping will be very useful for data visualization.
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There are plentiful explorable external factors that influence personals’ perceived ease of use (PEOU) and perceived usefulness (PU) towards a technology. In specify, I exemplify the role of geoinformatics technology in disease surveillance as one of the prominent technology platforms in public health program. I determine two external variables and explain how they connect to the technology acceptance model, they are the importance of job and supporting conditions.
Job importance is related to the characteristics of an organization’s business processes and the expected output of a particular job by enhancing the role of technology. Those aspects will strongly affect behavioral intention because of the demands to operate technology, although some researches also refer to the fitness between task and technology and cognitive fit as well. In my illustration, the health department needs experts in disease mapping for health program planning and then recruit at least data scientists who are able to demonstrate geoinformatics system-related tasks, or it is called ‘Geographic Information System (GIS)’. The same thing may also be applied in health-related faculties and research institutions because of the demand for advanced analysis.
Supporting condition means a circumstance in which the internal organization facilitates the workforce to execute technology-specific tasks and to overcome the organization’s demand. There are no limitations regarding the scope of supporting conditions as the spectrum of this external aspect is broad. Good internet connection, provision of GIS software, training, and mentoring, are the prime example that has a direct impact on PU and PEOU. Therefore, it is true to say that a lack of supporting conditions can avoid individuals from accepting the use of GIS-related technology for disease mapping.
I conclude that with the exemplification of using GIS for disease mapping, job importance, and supporting conditions are two external variables that affect people to accept technology in their working duties.
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Efficacy, effectiveness, and efficiency are three distinct principles in an experimental study, but they can be interrelated. I distinguish these three meanings and how they are respectively related.
Efficacy indicates to what extent an intervention can create a beneficial change or achieve certain results in ideal circumstances where all factors are controlled. This usually concerns the results of laboratory clinical trials conducted on a population where researchers can control the size, such as research that aims to assess the efficacy of a newly produced Covid-19 vaccine. The efficacy data generated from the experimental research will be useful for evaluating effectiveness.
In contrast to efficacy, effectiveness talks about to what extent the beneficial results/outcomes/changes are achieved from an intervention carried out in real conditions where the factors cannot be controlled. To illustrate, the government will definitely choose a type of vaccine with a high level of efficacy to be used in Covid-19 mass vaccination program. In the following months, an antibody serological survey was conducted to measure the health outcome in terms of the SARS-CoV antibody. Of course, a product with high efficacy, could not always match the non-ideal conditions that occur in real life. It is generally concluded that effectiveness will be versus efficacy because the effectiveness rate is lower than the efficacy.
Efficiency is about doing things in the most efficient way, whether in terms of time, effort, or money. In controlling Covid-19, the government can effectively apply the right vaccine, but not necessarily use an efficient method. Two comparison vaccines with a high efficacy rate may effective in producing high levels of antibodies, but if one is much more expensive than the other, the vaccine will not be considered efficient.
In conclusion, the three outcome measures are different but interrelated. They can illustrate a meaningful sequence and produce good evidence-based research for disease control program.
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Well, this is a challenging case study where I almost got stuck. I have my own opinion about what might potentially become a confounder, which is the socioeconomic level. Please correct me if I’m wrong, but I distributed my reasons by:
1. Socioeconomic level is related to the user’s ability to follow the purpose of contract tracing up to the end. Participants with a good socioeconomic level will be able to buy a data package or update their cell phones at any time.
2. The socioeconomic level of young people may be very identical because they are in a moment that really needs communication devices.
3. Socioeconomic level is not an effect of age (is not a by-product of age). Prosperous children may come from their rich parents. And the elders may be considered at a good socio-economic level because at that age they have become rich or are from good family backgrounds. -
I am now assigned as technical staff for the surveillance of PHEIC (Public Health Emergency of International Concern) in the Directorate General of Disease Prevention and Control under the Indonesia Ministry of Health. This office was one of the busiest agencies during Covid-19 instead of hospitals, airports, and quarantine settlements. So, it is true to acknowledge that here we are working for a service-oriented that is policy-driven. These are the core value we have to perform in any situation:
1. Service-oriented: understand and fulfill community needs, being fully friendly, humble, and never stop making improvements.
2. Accountable: carry out all duties honestly, responsibly, meticulously, high discipline and integrity.
3. Competent: increase self-competence to respond to-changing challenges. Help others learn, and demonstrate the best performance.
4. Harmonious: respect for diversity. Excites to offer assistance and build a harmonious working ecosystem.
5. Loyal: loyal to the country and legitimate government.
6. Adaptive: quickly adapt to changes. Continue to innovate and develop creativity, and act proactively.
7. Collaborative: provide opportunities for various parties to contribute and being open to working together. -
Before Universal Health Coverage (UHC), many districts created various ad-hoc social security schemes as the impact of decentralization which implied the heterogeneity of health system but exacerbated the equity gap. Then, the paradigm UHC had been started in 2014 to expand access to health services and protect people from the risk of poverty due to catastrophic spending. Five sources of funding come from contributing members, formal sector employees (employers, family members, and retirees), non-contributing members (people who are living in poverty and are covered by Government), governmental contributions from tax, and grants from overseas development agencies.
At the inception stage, this scheme had immediately covered 117 million people, then, it reached 229 million at the end of 2021 (data from social security agency). Enrolment in lower and middle-income families was significant progress, especially for those at high risk of chronic disease. This is incredible because, before UHC, most people in low-income and vulnerable groups had less access to health care than middle and high-income families. And distinct from private insurance providers, this newly launched UHC scheme enables people with pre-existing chronic diseases to enroll in it. So, over the 9 years of implementation, the most consistent achievement is the use of health services was higher among people on low and middle incomes and those in rural areas rather than among people on high incomes.
As the impact of greater health financing for secondary and tertiary hospital costs especially for the poor, it will contribute to long-term financial sustainability. And this risk will be deteriorated by another consequence if the UHC members decide to discontinue payment (particularly from those who come from the informal sector). To maintain sustainability which depends seriously on member contribution, my country needs a new mechanism for stricter healthcare reimbursement process well as expansion on the enrolment. Increases in contribution fees occur but this only led to controversy. And back to the concept of health priority setting, we should prioritize promotive and preventive care in any health system in order to reduce non-communicable disease risk in the long run.
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My country has a narrow of health informaticians and is facing a lack of influx to this profession. This position seems unpopular due to the shortage of participating students in this particular educational program and the unmet workforce planning supply.
Over the last two decades, Indonesia’s labor force has been shifting from high school to undergraduate holders. However, although health informatics could also be part of majors in public health undergraduate programs, but students participating in this program are always countable and rare. To illustrate based on my own experience, among 234 students in the same cohort, those who select health informatics were only 4%.
Limited research to forecast the future digital health, to assess workforce needs, and to review the distribution of health informatics graduated students are other prime causes, which affect unmet planning for this profession. For instance, in my ministerial agency, there is no professional nomenclature on health informaticians. Professions with similar job descriptions are overlapping, such as data analytics, statisticians, and data managers. In the private sector, hospital tends to recruit IT professionals and medical records officers though they need to understand health care and organizational behavior. Companies and startups, including those who invest in health, are now recruiting data scientists or data analysts who are focusing on making predictions.
To conclude, participating students and workforce planning are the two shortcomings that make health informatics prospects seem not marketable. I personally suggest that we can start by creating a better ecosystem of health informatics students and bringing research evidence into advocacy for careful labor planning.
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If I were the owner of data or metadata, perhaps data sharing is one of the imperative obligations I need to undertake as it is a public pretension in my own country where transparency should be mainstreamed. Even in the government sector, data and information disclosure has become the main parameter for bureaucratic reform. So, I absolutely would be thinking about it.
However, apart from the purpose of maintaining public trust and generating knowledge through research, two important aspects I have to consider are 1) what structure of data I need to disclose and 2) what is the aim of sharing process. I strongly agree that disclosable data should be fully and openly shared in a standardized format to simplify follow-up activities, and the most important thing is that the authenticity, quality, and integrity of the personal data must be preserved.
For me, good data-sharing practices should actualize a guiding principle what I called FAIR-CARE which is the abbreviation of findable, accessible, interoperable, reusable, collective benefit, authority to control, responsibility, and ethics. The next concern is, important for me as the owner of dataset to possess a mechanism to carefully share the dataset. This digital era strongly depends on online publishing. But I assume that the use of online platforms such as organizational websites, social media, and online messengers do not always become the safest and most appropriate channels for information sharing. For the general data that illustrates a certain situation on a broader scale (such as disease incidence in a district or province), we may disclose it through an online system. But the narrower the data scale, the stricter mechanism we have to share and that should be disclosed upon a request.
To answer the concern about pay or not to pay, it relies on the country’s regulation and institutional policy. Here as I work in a governmental office, making data commercially available is strictly prohibited in my institution. The cash of any public services have been regulated, but service related to sharing dataset has zero cost because the priority is focusing on how to consistently actualize the data-sharing procedure and mechanism.
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AGE-SPECIFIC MORTALITY RATE
Age-specific mortality rate is defined as the number of deaths in a particular age/age group in a specific geographical area (city, state, country) per 1.000 population in the same age/age group. It can be calculated by dividing those dying at a particular age/age group by the population in that age/age group.
Age-specific mortality rate is used to suggest specific causes of death according to the persistent patterns of current health problems (disease risks and disease events) at that age/age group in a particular community.
Most countries illustrate the pattern of age-specific mortality rate on the J-shaped, meaning that mortality is high among infants, children under 5, and young children. Then, it drops rapidly until age groups 5-14. The curve increases exponentially above age 35 and skyrockets after age 65.
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Sir Navin
I experienced the same situation when installing INLA for the first time. Then, I employed another alternative instruction mentioned by https://www.r-inla.org/. Here is the code:install.packages(“INLA”,repos=c(getOption(“repos”),INLA=”https://inla.r-inla-download.org/R/stable”), dep=TRUE)
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I like that you raise a point about methodological complexity. Epidemiology in place variable, known as spatial epidemiology, contains a lot of complex statistical methods computed from either frequentism or Bayesianism (belief and probability) approach.
Spatial epidemiology allows us to display maps of disease risk that are leveraged for the decision-making process. These risk maps help identify areas with higher or lower disease risks, enabling policymakers to target interventions.
However, not all knowledge users, even decision-makers may fully realize and adopt the importance of statistical methods that work behind the map. When comparing different areas to find out high-risk distribution, people might use two or more common epidemiological measurements such as absolute numbers, rates, or proportions, then exhibit the number on a map to make assumption.
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Hi Kawin, your dashboard is incredible. The controls menu(s) allow us to filter and visualize the data based on interest. My input is about the control menu to select the province which seems to take a lot of space in the dashboard. Using the drop-down list would save space, and you can add other charts in the remaining areas. Best.
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Hi, I like your chosen dashboard. It is also true to say that the given colour in the bar chart is prone to missleading information for the readers. But overall, the dashboard style is inspiring as WHO is able to demonstrate a dashboard with proportionate verbal, numerical, and art context. What I satisfy with this dashboard there’s a delicate balance to using pastel color to keep it simple.
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Of course, important to be aware of the agreement to avoid misuse of data. I think it is indispensable particularly before the applicants ask for the specific datasets, they should be asked for this point. An application form to request data should attach clear information about do, don’t, and consequences.
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Appreciate that you have shared comprehensive approach in responding to the challenges in the big data era. While I pay too much attention on human resource capacity, other focuses such as collaboration, partnerships and governance are also important. It cannot be denied that the development of big data and AI may be faster than what humans can do.
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