An infectious way to teach students about outbreaks

Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates

Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity.

Integrating Risk Assessment and Decision-Making Methods in Analyzing the Dynamics of COVID-19 Epidemics in Davao City, Mindanao Island, Philippines

The COVID-19 pandemic has become a public health crisis in the Philippines and the attention of national and local health authorities is focused on managing the fluctuating COVID-19 cases. This study presents a method that integrates risk management tools into health care decision-making processes to enhance the understanding and utilization of risk-based thinking in public health decision making. The risk assessment consists of the identification of the key risk factors of the COVID-19 contagion via bow-tie diagrams. Second, the safety controls for each risk factor relevant to the Davao City context are taken into account and are identified as barriers in the bow-tie. After which, the prioritization of the identified COVID-19 risks, as well as the effectiveness of the proposed interventions, is performed using the analytic hierarchy process. Consequently, the dynamics of COVID-19 management initiatives were explored using these priorities and a system of ordinary differential equations. Our results show that reducing the number of COVID-19 fatalities should be the top priority of the health authorities. In turn, we predict that the COVID-19 contagion can be controlled and eliminated in Davao city in three-month time after prioritizing the fatalities. In order to reduce the COVID-19 fatalities, health authorities should ensure an adequate number of COVID-ready ICU facilities. The general public, on the other hand, should follow medical and science-based advice and suspected and confirmed COVID-19 patients should strictly follow isolation protocols. Overall, an informed decision-making is necessary to avoid the unwanted consequences of an uncontrolled contagion.

Evaluation through outbreak simulation exercise points to the need for considerable improvement in the capacity of peripheral health workers for outbreak detection and response, South India, 2018

Background:

Outbreaks are emergencies, requiring skilled peripheral health workers in the health system. Given the lack of evaluation of the knowledge and practices of peripheral health workers regarding outbreak investigation and response, we surveyed to estimate the performance level of health workers in outbreak detection and response.

Methods:

We developed a simulation exercise based on hepatitis and fever outbreak to ascertain knowledge and skills in outbreak detection and response. Following a pilot test and with inputs from public health experts, we finalized the instrument in the local language. The simulation exercise was self-administered among all health inspectors (HI) (n = 39) from a district in South India responsible for outbreak investigation. We collected sociodemographic factors, training, education level, awareness about the surveillance program, outbreak triggers, and prior experience with an outbreak. We assigned a score of 0.25 for each correct response (range 0 to 10.75). We categorized a score of <75% as poor performance. The academic ethics committee of ICMR-National Institute of Epidemiology approved the protocol.

Results:

All the HIs were male except one. Median age is 51 years (Range: 37.5–54). The median years of service is 12 (range 5.3 to 23). Twenty-two received training, and fifteen had prior exposure to an outbreak in the previous year. The overall performance of HIs was poor, with the highest mark being below 40%. The median score in the section of history taking was 0.25 [interquartile range (IQR) 0–0.5], 31% (n = 12) scored zero. The median score in the section of data entry, analysis, and outbreak detection was 0.25 (0–0.25), 28% (n = 11) scored zero. The median score in the section of outbreak response was 0.75 (IQR 0.75–1.13), 5% (n = 2) scored zero.

Conclusion:

The HIs performed poorly in outbreak preparedness and response. We recommend improving their performance through field-epidemiology training and regular field or facility-based evaluations.

The experiences of using polio outbreak simulation exercises to strengthen national outbreaks preparedness and response plans in sub-Saharan Africa

Introduction

globally, by 2020 the paralytic poliomyelitis disease burden decreased to over 99% of the reported cases in 1988 when resolution 41.8 was endorsed by the World Health Assembly (WHA) for global polio eradication. It is clearly understood that, if there is Wild Poliovirus (WPV) and circulating Vaccines Derived Poliovirus (cVDPV) in the world, no country is safe from polio outbreaks. All countries remain at high risk of re-importation depending on the level of the containment of the types vaccine withdrawn, the laboratory poliovirus isolates, and the population immunity induced by the vaccination program. In this regard, countries to have polio outbreak preparedness and response plans, and conducting the polio outbreak simulation exercises for these plans remain important.

Methods

we conducted a cross-section qualitative study to review to 8 countries conducted polio outbreak simulation exercises in the East and Southern Africa from 2016 to 2018. The findings were categorized into 5 outbreak response thematic areas analyzed qualitatively and summarized them on their strengths and weaknesses.

Results

we found out that, most countries have the overall technical capacities and expertise to deal with outbreaks to a certain extent. Nevertheless, we noted that the national polio outbreak preparedness and response plans were not comprehensive enough to provide proper guidance in responding to outbreaks. The guidelines were inadequately aligned with the WHO POSOPs, and IHR 2005. Additionally, most participants who participated in the simulation exercises were less familiar with their preparedness and response plans, the WHO POSOPs, and therefore reported to be sensitized.

Conclusion

we also realized that, in all countries where the polio simulation exercise conducted, their national polio outbreak preparedness and response plan was revised to be improved in line with the WHO POSOPs and IHR 2005. we, therefore, recommend the polio outbreak simulation exercises to be done in every country with an interval of 3-5 years.

An artificially simulated outbreak of a respiratory infectious disease

Background

Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases.

Methods

We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number (R₀), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed.

Results

Once the R₀ exceeded 1.5, the median attack rate increased sharply; when R₀ = 3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84–8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R₀, TOI, IOI, and IR were 0.61, 0.17, 0.45, and − 0.27, respectively.

Conclusions

The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.