Infection Control for Emerging Infectious Diseases in Developing Countries and Resource-Limited Settings

Rapid expansion of temporary, reliable airborne-infection isolation rooms with negative air machines for critical COVID-19 patients

More airborne-infection isolation rooms are needed in centers that treat severely affected coronavirus 2019 patients. Wards and rooms must be carefully checked to ensure an ample supply of medical air and oxygen. Anterooms adjacent to airborne-infection isolation rooms are required to maintain pressure differentials and provide an area for donning/doffing or disinfecting medical equipment.

Emerging infectious diseases: Focus on infection control issues for novel coronaviruses (Severe Acute Respiratory Syndrome-CoV and Middle East Respiratory Syndrome-CoV), hemorrhagic fever viruses (Lassa and Ebola), and highly pathogenic avian influenza viruses, A(H5N1) and A(H7N9)

Over the past several decades, we have witnessed the emergence of many new infectious agents, some of which are major public threats. New and emerging infectious diseases which are both transmissible from patient-to-patient and virulent with a high mortality include novel coronaviruses (SARS-CoV, MERS-CV), hemorrhagic fever viruses (Lassa, Ebola), and highly pathogenic avian influenza A viruses, A(H5N1) and A(H7N9). All healthcare facilities need to have policies and plans in place for early identification of patients with a highly communicable diseases which are highly virulent, ability to immediately isolate such patients, and provide proper management (e.g., training and availability of personal protective equipment) to prevent transmission to healthcare personnel, other patients and visitors to the healthcare facility.

Risks to healthcare workers with emerging diseases: lessons from MERS-CoV, Ebola, SARS, and avian flu

PURPOSE OF REVIEW

Several viral diseases have emerged and impacted healthcare systems worldwide. Healthcare personnels (HCPs) are at high risk of acquiring some emerging infections while caring for patients. We provide a review of risk factors, evidence of infection in HCPs, and prevention strategies with Middle East respiratory syndrome coronavirus, Ebola virus disease (Ebola), severe acute respiratory syndrome (SARS), and avian influenza.

RECENT FINDINGS

HCP-related infections with Middle East respiratory syndrome coronavirus, Ebola, and SARS have been reported among 1-27%, 2.5-12%, and 11-57% of total cases, respectively. The case fatality rate of Ebola in HCPs has been reported up to 73%. The WHO guidelines for the global surveillance of SARS were developed in 2004 and used as a template for other emerging diseases preparedness. Risks to HCPs with emerging diseases are related to inappropriate and insufficient infection control measures during an initial encounter, at the beginning of outbreak and with an overwhelming number of patient cases. To date, there are no reports of avian influenza transmission to HCPs from affected cases.

SUMMARY

Early and rapid detection of suspected infected patients with communicable diseases along with appropriate infection control practice, education, national and global preparedness guidelines would help to prevent disease transmission to HCPs.

Application of a novel grey self-memory coupling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea

OBJECTIVE

In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China.

METHODS

The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates.

RESULTS

Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China.

CONCLUSION

The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.

Issues relevant to the adoption and modification of hospital infection-control recommendations for avian influenza (H5N1 infection) in developing countries

The reemergence of avian influenza (H5N1 infection) has heightened concern for a potential human influenza pandemic. Recommendations regarding preparation for a global avian influenza pandemic are available, and it is imperative that health care workers participate in preparedness planning and training. In developing countries, health care worker preparedness training should address the modes of avian influenza transmission and specify how to implement appropriate infection-control strategies to prevent and control the spread of avian influenza. We provide evidence for avian influenza transmission methods and identify prevention strategies relevant to infection control for hospitals in developing countries. Pandemic influenza preparedness plans must include health care administrative support, mechanisms to rapidly create temporary isolation facilities, systems to restrict access to exposed health care workers, and plans to involve specialists to screen and identify cases early, to provide for continuous monitoring to ensure adherence to optimal infection-control practices, and to provide regular feedback to health care workers.