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Modeling Infectious Diseases in Dynamic Populations with Relocation and Refugeeism

Periodic Reporting for period 1 - MIDIDP (Modeling Infectious Diseases in Dynamic Populations with Relocation and Refugeeism)

Okres sprawozdawczy: 2018-04-01 do 2020-03-31

Infectious disease outbreaks such as the new coronavirus disease (Covid-19) pandemic brings a great burden on societies in terms of lives lost, economic stagnation lost labor, etc. To better prepare ourselves for these outbreaks, we need to make good planning of our resources such as hospitals, health workers, medical equipment, etc. The modeling and simulation method has a great advantage over other methods in terms of scenario analysis for planning, preparedness, and response. In this project, we propose to create a unified framework to combine different data sources to create realistic simulation software to model the spread of infectious diseases. An important aspect of the population nowadays is the refugees living in camps or cities in many European countries. Any preparedness and response activity against an infectious disease should include them as an important player due to their possibly different culture, vaccination status, immunological history, etc.

Overall objectives of MIDIDP project are:
1. To prepare a complete synthetic population as a case study.
2. To simulate infectious diseases by using the synthetic population
3. To calibrate and verify the simulation and to test different containment scenarios and interventions for infectious diseases for different countries and regions with and without refugees

The overall conclusions of the project are:
We developed an agent-based model to realistically simulate an infectious disease epidemic in a geographic area, which takes many data sets as input including age-sex distributions, family size, local and refugee population density, disease/pathogen information. In doing this we created a generic framework (a protocol) on how to go from input data sets to final simulation results that can provide guidance for public health authorities to respond to infectious diseases. This framework is designed in such a way that anybody with basic knowledge of epidemiology and programming can utilize it to create simulations for their geographic area and population. Our initial results show that among all many input parameters the family size is the most sensitive one in the final size of the epidemic for vaccine-preventable childhood diseases such as measles and population density is an additional sensitive parameter in diseases with no prior immunity such as pandemic influenza and the new coronavirus disease Covid-19.
Specific activities to reach each objective are
1. In a unified framework of synthetic population, we combined different data sources including location-dependent density data from demographic statistics from Turkish Statistical Institute, statistics about refugees from the Turkish Migration Office as well as the new large-scale survey administered on Syrian population (the largest group of refugees in Turkey forming more than 90% of the refugee population) by the Hacettepe University Institute of Population Studies. The synthetic population includes synthetic individuals that are similar to real individuals at the aggregate level that can be attributed through different demographic distributions such as gender, age, occupation, household/workplace/school size, etc. For this objective, we created a set of guidelines that can be used to generate synthetic populations for other European countries.
2. The primary modeling software platform FRED that we were planning to use was commercialized last year. Although a community edition is still available, it is less-frequently updated so Dr. Guclu created an agent-based model similar to FRED but with simpler functionality. The new model is capable of using synthetic population data and creating infectious disease scenarios (influenza, measles, Covid-19) as well as simulating interventions such as isolation, quarantine, antivirals, etc.
3. Dr. Guclu has been assigned as a consultant for the local Istanbul Pandemic Coordination Board and also asked to be a statistician/modeler at the newly formed Ministry of Health board, Social Sciences Board. Dr. Guclu is using his expertise and the results from the MIDIDP project to guide public health authorities in assessing the effectiveness of interventions and devising new ones. With the new data sources made available to him, Dr. Guclu is simulating realistic Covid-19 scenarios in Turkey that can be extended into other European countries as well.

Overview of the results and Exploitation and Dissemination

The MIDIDP Project aimed at creating a realistic model to simulate infectious disease spread in different parts of Europe. The main output of the project was a framework to prepare the simulation by using publicly available data sources. The framework can be applied in any other European country to simulate infectious diseases such as pandemic influenza, measles, and Covid-19.

Dr. Guclu has been an ardent supporter of disseminating the results of any project to the public. He has been trained in an intensive science communication program at Carnegie Science Center of the City of Pittsburgh. As part of the dissemination activities of the MIDIDP Project, his exhibition Germ Troopers was accepted for the Science if Wonderful event in Brussels. He participated in the event for two days to teach children and adults how germs spread and how to wash hands properly.

Dr. Guclu was a member of the founding board of Istanbul Medeniyet University (IMU) Technopark up until last year. Dr. Guclu has been using the contacts at IMU Technopark to found a company to exploit the work of MIDIDP. In January 2020, Dr. Guclu and his colleagues at IMU started a new Master of Science program called “Biological Data Science”. He is now the program director with 30 students from various disciplines. Dr. Guclu has four students he has been advising on their theses. The MIDIDP Project has served as a source of ideas for these students' theses.

Through the MIDIDP project, Dr. Guclu has made connections with public health authorities in Turkey both at the local and national levels that are the primary “customers” for the kind of work done in the project. Since last January he has been also so active in Twitter to disseminate the results of the project to more than 5200 followers including the members of the National Pandemic Preparedness Board (@hguclu) as well as to raise awareness about the outbreak and to increase compliance of the public to accept the measures and interventions. Instead of primarily retweeting other people’s messages he tries to create original content backed up by science and the project MIDIDP was very instrumental to give him that edge.
The MIDIDP project provides a unique opportunity for researchers to simulate infectious diseases on realistic synthetic populations. It can be easily fed with country-specific input data and used other countries to test different infectious disease emergency and intervention scenarios. The output of the MIDIDP project such as the number of cases, hospitalized or intubated, can be converted into labor lost and thus can be used to assess the economic burden of outbreaks on businesses and countries. The synthetic populations generated with its location-specific features can also be used for different purposes in different fields including city planning, preparedness for other emergencies, and social sciences.
Outreach at Science is Wonderful event in Brussels
Hasan Guclu twitter profile used to disseminate the results and public outreach
One of the infectious disease models used in the project
Outreach at Science is Wonderful event in Brussels
MSCA fellow of the week Hasan Guclu
The relationship between poverty index and Covid-19 cases for many countries